import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib as mpl
import seaborn as sns
import re
from sklearn.cluster import KMeans
import folium
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split 
from sklearn import metrics
from datetime import date
from six import StringIO  
from IPython.display import Image  
import pydotplus

# Do not touch me
df_fines = pd.read_csv("fines.csv", sep=',',skiprows=0,skipfooter=1, na_values=None,engine='python')
df_latlons = pd.read_csv("latlons.csv", sep=',', skiprows=0, skipfooter=1, na_values=None,engine='python')
df_addresses = pd.read_csv("addresses.csv", sep=',', skiprows=0, na_values=None,engine='python')

len(df_fines.index)

62492

df_fines['payment_status'] = df_fines['payment_status'].map(
    lambda x: 0 if x == 'NO PAYMENT APPLIED'
              else 1 if x == 'PARTIAL PAYMENT APPLIED'
              else 2
    )

not_responsible_tags = r'Not responsible by Dismissal\
                        |Not responsible by City Dismissal\
                        |Not responsible by Determination'
responsible_tags = r'Responsible by Default\
                    |Responsible by Determination\
                    |Responsible by Admission'
omitted_tags = (r'PENDING JUDGMENT|SET-ASIDE \(PENDING JUDGMENT\)')

# Filter out fines that were not resolved
df_fines = df_fines[df_fines.disposition.str.contains(omitted_tags) == False]

# Change labels to boolean vals
df_fines['disposition'] = df_fines['disposition'].map(
    lambda x:
        "Not responsible" if x in not_responsible_tags
        else "Responsible")

# Remove fines that were dismissed
df_fines = df_fines[df_fines['disposition'] == "Responsible"]

# Change 0/1 compliance to boolean
df_fines['compliance'] = df_fines['compliance'].map(
    lambda x:
        False if x == 0.0
        else True)

# Change violation code to category type
df_fines['violation_code'] = df_fines['violation_code'].astype('category')

len(df_fines.index)

42023

df_fines.columns

Index(['ticket_id', 'agency_name', 'inspector_name', 'violator_name',
       'violation_street_number', 'violation_street_name',
       'violation_zip_code', 'mailing_address_str_number',
       'mailing_address_str_name', 'city', 'state', 'zip_code',
       'non_us_str_code', 'country', 'ticket_issued_date', 'hearing_date',
       'violation_code', 'violation_description', 'disposition', 'fine_amount',
       'admin_fee', 'state_fee', 'late_fee', 'discount_amount',
       'clean_up_cost', 'judgment_amount', 'payment_amount', 'balance_due',
       'payment_date', 'payment_status', 'collection_status',
       'grafitti_status', 'compliance_detail', 'compliance'],
      dtype='object')

df_fines['mailing_address_str_name'] = df_fines['mailing_address_str_name'].astype(str)
df_fines['violation_street_name'] = df_fines['violation_street_name'].astype(str)
df_fines['violator_name'] = df_fines['violator_name'].astype(str)

df_fines['ticket_issued_date'] = pd.to_datetime(df_fines['ticket_issued_date'])
df_fines['ticket_year'] = df_fines['ticket_issued_date'].dt.year
df_fines['ticket_month'] = df_fines['ticket_issued_date'].dt.month
df_fines['ticket_day'] = df_fines['ticket_issued_date'].dt.day
df_fines['ticket_day_week'] = df_fines['ticket_issued_date'].dt.day_of_week

#['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
df_fines = pd.get_dummies(df_fines,columns=['ticket_day_week'])
df_fines = df_fines.rename(columns={'ticket_day_week_0':'isMonday',
                                    'ticket_day_week_1': 'isTuesday',
                                    'ticket_day_week_2':'isWednesday',
                                    'ticket_day_week_3':'isThursday',
                                    'ticket_day_week_4':'isFriday',
                                    'ticket_day_week_5':'isSaturday',
                                    'ticket_day_week_6':'isSunday'})

df_fines.columns

Index(['ticket_id', 'agency_name', 'inspector_name', 'violator_name',
       'violation_street_number', 'violation_street_name',
       'violation_zip_code', 'mailing_address_str_number',
       'mailing_address_str_name', 'city', 'state', 'zip_code',
       'non_us_str_code', 'country', 'ticket_issued_date', 'hearing_date',
       'violation_code', 'violation_description', 'disposition', 'fine_amount',
       'admin_fee', 'state_fee', 'late_fee', 'discount_amount',
       'clean_up_cost', 'judgment_amount', 'payment_amount', 'balance_due',
       'payment_date', 'payment_status', 'collection_status',
       'grafitti_status', 'compliance_detail', 'compliance', 'ticket_year',
       'ticket_month', 'ticket_day', 'isMonday', 'isTuesday', 'isWednesday',
       'isThursday', 'isFriday', 'isSaturday', 'isSunday'],
      dtype='object')

df_fines['isOwner'] = np.where(df_fines['mailing_address_str_name'].astype(str).str.upper() == df_fines['violation_street_name'].astype(str).str.upper(), 1,0)
df_fines['isOwner'] = np.where(df_fines['mailing_address_str_number'].astype(float) == df_fines['violation_street_number'].astype(float),1,0)

df_fines[df_fines['isOwner'] == 1]

misspellings = {
    'INVESTMENT, ACORN' : ['INVESTMENT CO., ACORN',
                           'Investment, Acorn',
                           'ACORN INVESTMENT, *',
                           'INVESTMENTS, ACORN',
                           'Acorn Investments, *',
                           'COMPANY, ACORN INVESTMENT',
                           'ACORN INVESTMENTS, *',
                           'CO., ACORN INVESTMENT',
                           'Investment Co., Acorn',
                           'investment, acorn',
                           'INVESTMENT CO, ACORN',
                           'ACORN INVESTMENT CO., .',
                           'Company, Acorn Investment',
                           'ACORN INVESTMENT COMPANY, .',
                           'Co., Acorn Investment',
                           'INVESTMENT COMP., ACORN',
                           'COMPANY, ACORN  INVESTMENT',
                           'ACORN INVESTMENT CO., *',
                           'Co, Acorn Investment',
                           'INVESTMENT CO. , ACORN',
                           'Investment Company, Acorn',
                           'INVESTMENT COMPANY, ACORN',
                           'COMP., ACORN INVESTMENT',
                           'ACORN INVSTM. CO., .',
                           'CO, ACORN INVESTMENTS',
                           'CO, ACORN INVESTMENT',
                           'INVETMENT CO., ACORN',
                           'CO., ACORN  INVESTMENT',
                           'Co., Acorn Investment-',
                           'COMP, ACORN INVERTMENT',
                           'Investment, Acorns',
                           'Investment Co, Acorn',
                           'acorn investment, *',
                           'ACORN INVESMENTS, *',
                           'INNESTMENT, ACORN',
                           'ACORN INVESTMENT, CO',
                           'IVESTMENT, ACORN',
                           'INVESMENT, ACORN',
                           'Acorn Investment, *',
                           'ACORN  INVESTMENTS  CO., .',
                           'c/o ACORN INV., OAK MANAGEMENT',
                           'Investment, Company Acorn',
                           'COMPANY, ACORN   INVESTMENT',
                           'INVESTMENT CO, A CORN',
                           'INVESTMENT CO., A CORN',
                           'INVESTMENT COMPANY, A CORN',
                           'INVESTMENT, A CORN'],

    'BANK, WELLS FARGO' : ['BANK MINNESOTA, WELLS FARGO',
                           'BANK MINNESOTA, WELLS  FARGO',
                           'BANK MINNSOTA, WELL FARGO',
                           'BANK, W ELLS FARGO',
                           'BANK, WELL FARGO',
                           'BANK, WELLS  FARGO',
                           'BANK, WELLS FARGO',
                           'FARGO BANK, WELLS',
                           'FARGO HOME MORTGAGE, WELLS',
                           'FARGO, WELLS',
                           'Fargo Bank, Wells',
                           'MINNESOTA, WELL FARGO BANK',
                           'MINNESOTA, WELLS   FARGO BANK',
                           'WELL FARGO BANK MINNESOTA, .',
                           'WELLS  FARGO  BANKS  MINNESOTA, .'
                           'WELLS FARGO BANK, .',
                           'MINNESOTA, WELLS FARGO BANK,',
                           'BANK, N.A., WELLS FARGO',
                           'BANK  MINNESOTA, WELLS FARGO',
                           'MINNESOTA, WELLS FARGO BANK',
                           'Bank, Wells Fargo',
                           'BANK, NA, WELLS FARGO',
                           'C/O OCWEN FEDERAL BANK, FSD., WELLS FARGO BANK-MINN.',
                           'BANK,NA TRUSTEE, WELLS FARGO',
                           'HOME MORTGAGE INC, WELLS FARGO',
                           'BANK N. A., WELLS FARGO',
                           'WELLS FARGO HOME MTG., INC., .',
                           'for option one mtg., wells fargo bank natl.assoc trustee',
                           'TRUSTEE, WELLS FARGO BANK',
                           'Bank,NA, Wells Fargo',
                           'for option one mtg. loan, wells fargo bank m natl.assoc. trustt',
                           'HOME MORTGAGE, WELLS FARGO',
                           'BANK  MINNESOTA  NA, WELLS FARGO',
                           'MINNESOTA, N.A., WELLS FARGO BANK',
                           'MINNNESOTA, N.A., WELLS FARGO BANK',
                           'BANK,,N.A., WELLS FARGO',
                           'BANK,N.A., WELLS FARGO',
                           'BANK , WELLS FARGO',
                           'WELLS FARGO HOME MORTGAGE, INC, .',
                           'BANK- MINNESOTA, WELLS FARGO',
                           'MORTGAGE, INC., WELLS FARGO  HOME',
                           'MTG INC, WELLS FARGO  HOME',
                           'MINNESOTA, WELLS FARGO',
                           'MORTGAGE, INC, WELLS FARGO HOMES',
                           'BANK-MINNESOTA, WELLS FARGO',
                           'NATIONAL ASSOCIATION, WELLS FARGO  MINNESOTA',
                           'HOME  MORTGAGA, WELLS FARGO',
                           'WELLS FARGO HOME MTG.. INC, .',
                           'C/O OCWEN FEDERAL BANK FSB, WELLS FARGO BANK MINN.',
                           'NATIONAL  ASSC, WELLS FARGO BANK MINNESOTA',
                           'MORTGAGE, INC, WELLS FARGO HOME',
                           'AMERICA INC, WELLS FARGO FINANCIAL',
                           'BANK  N.A, WELLS FARGO',
                           'BANK, N.A, WELLS FARGO',
                           'MORTGAGE INC., WELLS FARGO HOME',
                           'BANK MINN., WELLS FARGO',
                           'WELLS FARGO BANK MINNESOTA, .',
                           'MORTGAGE, INC., WELLS FARGO HOME',
                           'BANK MINNESOTA NATIONAL ASSOC., WELLS FARGO',
                           'BANK OF MINESOTA, WELLS FARGO',
                           'BANK N.A., WELLS FARGO',
                           'MORTGAGE, WELLS FARGO HOME',
                           'GRP/AG REAL ESTATE ASSET TRUST, WELLS FARGO MINNESOTA NATL ASSOC. (TRUSTEE)',
                           'GRP/AG REAL ESTATE ASSET TRUST, WELLS FARGO MINNESOTA NATL.  ASSOC (TRUSTEE)',
                           'MORTGAGE INC, WELLS FARGO',
                           'BANK MINNISOTA, WELLS FARGO',
                           'MINNESOTA, WELLS FARGO  BANK,',
                           'WELLS  FARGO  BANKS  MINNESOTA, .',
                           'WELLS FATGO BANK MINN., .',
                           'option one mtg.loan, wellsfarbo bank , Minnesota natl.assoc.,trustee for'],
    'MANAGEMENT CO., WINGATE' : ['MANAGEMENT CO., WINGATE',
                                 'MANAGEMENT, WINGATE',
                                 'APTS/WINGATE, INDUSTRIAL BUILDING',
                                 'WINGATE, EDDIE & JAMES',
                                 'MANAGEMENT CO,, WINGATE',
                                 'MANAGEMENT CO,., WINGATE',
                                 'WINGATE MGT CO. INC, *',
                                 'WINGATE, WILLIAM',
                                 'Wingate Mgt. Co. Inc., *',
                                 'WINGATE, NEIL',
                                 'MGT. CO, WINGATE',
                                 'Industrial Bldg. Apts/ Wingate, *'],
    'NEW YORK, BANK OF' : ['NEW YORK, BANK OF',
                           'NEW YORK, BANK  OF',
                           'NEW YORK, THE BANK OF',
                           'OF NEW YORK, THE BANK',
                           'BANK OF NEW YORK, .',
                           'NEW YORK , BANK OF',
                           'TRU, BANK OF NEW YORK',
                           'NEW YORK, BNK  OF',
                           'CERTFICATE HOLDERS CWABS2004-1, BANK OF NEW YORK TRUSTEE',
                           'OF NEW YORK, BANK',
                           'NEW YORK, THE BANK  OF',
                           'NEW YORK-TRUSTEE , BANK OF',
                           'NEW YORK TRUST, BANK OF',
                           'New York, The Bank of',
                           'THE BANK OF NEW YORK, *',
                           'NEW YORK, Bank of'],
    'BANK, DEUTSCHE' : ['TRUST CO, DEUTCHE  BANK NATIONAL',
                        'NATIONAL TRUST CO., DEUTSCHE BANK',
                        'NATIONAL TRUST, DEUTSCHE BANK',
                        'NATIONAL TRUST CO, DEUTSCHE BANK',
                        'TRUST CO., DEUTSCHE BANK NATIONAL',
                        'TRUST COMPANY, DEUTSCHE BANK NATIONAL',
                        'COMPANY AMERICAS, DEUTSCHE BANK TRUST',
                        'COMPANY, DEUTSCHE BANK NATIONAL TRUST',
                        "CO, DEUTSCHE BANK NAT'L TRUST",
                        'NATIONAL TRUST COMPANY, DEUTSCHE BANK',
                        'TRUST CO, DEUTSCHE BANK NATIONAL',
                        'NATIONAL  TRUST  CO., DEUTSCHE BANK',
                        'TRUST CO., DEUTSCHE BANK',
                        '& TRUST CO., DEUTSCHE BANK',
                        "TRUST CO, DEUTSCHE BANK NAT'L",
                        'TRUST CO., DEUTSCHE NATIONAL',
                        'DEUTSCHE BANK NATIONAL TRUST C, .',
                        "CO., DEUTSHE BANK NAT'L TRUST",
                        'BANK TRUST CO., DEUTSCHE NATIONAL',
                        'NAT. TRUST, DEUTSCHE BANK',
                        'National Trust Co., Deutsche Bank',
                        'BANK NATIONAL TRUST CO., DEUTSCHE',
                        'TRUST CO, DEUTSHE BANK NATION AL',
                        'TRUST CO AMERICAS, DEUTSCHE BANK',
                        'DEUTSCHE BANK NAT. TRUST CO., .',
                        'NATIONAL TRUST  CO., DEUTSCHE BANK',
                        'F/K/A BANKERS TRUST CO., DEUTSCHE BANK TRUST CO.  AMERICAS,',
                        'NATIONAL  TRUST, DEUTSCHE BANK',
                        'COMPANY, DEUTSCHE BANK TRUST',
                        'TRUST, DEUTSCHE  BANK NATIONAL',
                        'ASS, DEUTSCHE  BANK NATIONAL',
                        'BANK, DEUTSCHE NATIONAL',
                        'Company of the Americas, Deutsche Bank Trust',
                        'TRUST CO, DEUTSCHE  BANK NATIONAL',
                        "NAT'L TRUST CO., DEUTSCHE BANK",
                        'BANK NATIONAL TRUST CO, DEUTSCHE',
                        'BANK  NATIONAL TRUST CO, DEUTSCHE',
                        'C/O AMERE QUEST, DEUTSCHE BANK NATL  TRUST CO',
                        'NATIONAL  TRUST  CO., DEUTSHE BANK',
                        'NAT. TRUST CO., DEUTSCHE BANK',
                        'COMPANY, DEUTSCHE BANK  NATIONAL TRUST',
                        'NATIONAL. CO     , DEUTSCHE BANK',
                        'AMERICAS, DEUTSCHE BANK TRUST COMP.',
                        'DEUTSCHE BANK NATIONAL TRUST , 000000',
                        'DEUTSCHE BANK CO. OF CA, .',
                        'TRUST COMPANY, DEUTSCHE  BANK  NATIONAL',
                        'CO., DEUTSCHE BANK NATIONAL TRUST',
                        'CO, DEUTSCHE BANK NATIONAL TRUST',
                        'bank, deutsche',
                        'NATIONAL  TURST  CO., DEUTSCHE BANK',
                        'TRUST CO., DEUTSHE BANK NATIONAL',
                        'TRUST COMPANY AMERICAS, DEUTSCHE BANK',
                        'TRUST CO, DEUTSCHE BANK NAT'],
    'US Bank National Association' : ['ASSOCIATION, U.S. BANK NATIONAL',
                        'ASSOC, US BANK NATIONAL',
                        'ASSOC., US BANK NATIONAL',
                        'ASSOCIATION, US BANK NATIONAL',
                        'ASSOCIATION, U. S.BANK NATIONAL',
                        'ASSOC., U.S. BANK NATIONAL',
                        'C/O HOMEQ SERVICING CORP., US BANK NATIONAL ASSOC',
                        'C/O HOMEQ SERVICING CORP, US BANK NATIONAL ASSOC',
                        'ASS., US BANK NATIONAL',
                        'ASSOCATION, U.S. BANK NATIONAL',
                        'US BANK NATIONAL ASSOC, *',
                        'ASSOCIATION, US BANK NATIONSAL',
                        'N. A., US BANK',
                        'NA, US BANK',
                        'NATIONAL ASSOC., US BANK',
                        'NATIONAL ASSOCIATION, US BANK',
                        'TRUSTEE, US BANK NATIONAL ASSOCIATION',
                        'US BANH NTNL. ASSN., .',
                        'US BANK NTNL. ASSN., .',
                        'US Bank National Association'],  
    'Lasalle Bank National Association' : [
                        'NATIONAL ASSOCIATION, LASALLE BANK',
                        'ASSOCIATION, LASALLE BANK NATIONAL',
                        'LASALLE BANK, .',
                        'Bank, LaSalle',
                        'Associates, LaSalle Bank National',
                        'NATIONAL ASSOC., LASALLE BANK',
                        'LASALLE BANK MIDWEST, .',
                        'NATIONAL TRUST ASSOCIATION, LASALLE BANK',
                        'National Association, LaSalle Bank',
                        "TRUSTEE, LASALLE BANK NAT'L ASSOC.",
                        'NATIONAL, LASALLE BANK',
                        'BANK NATIONAL ASSOCIATION, LASALLE',
                        'ASSOC., LASALLE BANK NATIONAL',
                        'MIDWEST, LASALLE BANK',
                        'LASALLE, GLENN',
                        'LASALLE BANK NATL. ASSN., .',
                        'BANK, LASALLE',
                        'ASS., LASALLE BANK NATIONAL',
                        'LASALLE BANK NATL. ASSC., .',
                        'LASALLE BANK NATL. ASSCN., .'],
    'BANK, WASHINGTON MUTUAL': [
                        'BANK, WASHINGTON MUTUAL',
                        'MUTUAL BANK, WASHINGTON',
                        'WASHINGTON MUTUAL BANK, .',
                        'TROTT&TROTT,PC, WASHINGTON  MUTUAL BANK',
                        'BANK F.A., WASHINGTON  MUTUAL',
                        'MUTUAL BANK F.A., WASHINGTON',
                        
                        'BANK, WASHINGTON  MUTUAL',
                        'F A, WASHINGTON  MUTUAL BANK',
                        'BANK, F.A., WASHINGTON MUTUAL',
                        'BANK, FA, WASHINGTON  MUTUAL',],                   
    'FELLOWSHIP ESTATES LLC, .' : ['FELLOWSHIP ESTATES, L.L.C. , .',
                                   'FELLOWSHIP ESTATES, L.L.C., .',
                                   'FELLOWSHIP ESTATES, LLC, .'],
    'BANK, WACHOVIA' : ['BANK, WACHOVIA',
                        'Bank NA, Wachovia',
                        'OF DELAWARE, WACHOVIA BANK',
                        'WACHOVIA BANK, .'],
    'BANK, JP MORGAN CHASE' : ['BANK, J  P  MORGAN CHASE',
                               'BANK, J P MORGAN CHASE',
                               'BANK, J.P. MORGAN CHASE',
                               'BANK, JP MORGAN  CHASE',
                               'BANK, JP MORGAN CASE',
                               'BANK, JPMORGAN  CHASE',
                               'BANK, JPMORGAN CHASE',
                               'C-BASS MGT., J. P. MORGAN CHASE BANK',
                               'C-BASS MGT., J.P. MORGAN CHASE BANK',
                               'C/O OCWEN LOAN SERVICE LLC, J.P. MORGAN CHASE BANK',
                               'C/OHOMECOMINGS FINANCIAL NETWO, JP MORGAN CHASE BANK',
                               'C/Ohomecoming financial networ, JP.MORGAN CHASE BANK',
                               'CAPITAL  INC., MORGAN STANLEY ABS',
                               'CAPITAL, MORGAN STANLEY DEAN WITTER',
                               'CHASE  BANK, J P MORGAN',
                               'CHASE BANK TRUSTEE, JP MORGAN',
                               'CHASE BANK, J P  MORGAN',
                               'CHASE BANK, J P MORGAN',
                               'CHASE BANK, J. P. MORGAN',
                               'CHASE BANK, J.P. MORGAN',
                               'CHASE BANK, JP MORGAN',
                               'CHASE BANK, N.A. AS TRUSTEE, J. P. MORGAN',
                               'CHASE BANK-TRUSTEE, JPMORGAN',
                               'CHASE, J P MORGAN',
                               'CHASE, JP MORGAN',
                               'Chase Bank, JP Morgan',
                               'J P MORGAN CHASE BANK, .',
                               'MORGAN CAHSE BANK, JP',
                               'MORGAN CHASE, JP',
                               'TROTT&TROTT P.C., JP MORGAN CHASE BANK IN C/O'],
        'MORTGAGE ELECTRONIC' : ["INC, MORTGAGE ELECTRONIC REG. SYS'S",
                                 "INC, MORTGAGE ELECTRONIC SYS'S",
                                 'MORTGAGE ELECTRONIC  REG. SYS., .',
                                 'MORTGAGE ELECTRONIC REG.SYS.,I, .',
                                 'MORTGAGE ELECTRONIC REGIST., *',
                                 'REG SYS. INC, MORTGAGE ELECTRONIC',
                                 'REG. SYS., INC., MORTGAGE ELECTRONIC',
                                 'REG. SYSTEM, MORTGAGE ELECTRONIC',
                                 'REGISITRATION SYS. INC, MORTGAGE ELECTRONIC',
                                 'REGIST  SYSTEM, MORTGAGE ELECTRONIC',
                                 'REGISTARTION SYSTEM INC., MORTGAGE ELECTRONIC',
                                 'REGISTATION  SYSTEM  INC., MORTGAGE ELECTRONIC',
                                 'REGISTATION SYSTEM INC, MORTGAGE ELECTRONIC',
                                 'REGISTRARION SYSTEM INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION   INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION  INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION  SYSTEM   INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION  SYSTEM  INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION  SYSTEM INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION  SYSTEM, MORTGAGE ELECTRONIC',
                                 'REGISTRATION  SYSTEM,INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SY, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SY., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYM. INC., MORTGAGE ELECTRONIC',
                                 "REGISTRATION SYS'S, INC., MORTGAGE ELECTRONIC",
                                 'REGISTRATION SYS, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYS. INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYS., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM  INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM  INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM,INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM,INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS  INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS, INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS, INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS,INC  , MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS,INC, MORTGAGE ELECTRONIC',
                                 'REGISTRATION SYSTEMS,INC, MORTGAGE ELECTRONICS',
                                 'REGISTRATION SYSTEMS,INC., MORTGAGE ELECTRONIC',
                                 'REGISTRATION, MORTGAGE ELECTRONIC',
                                 'REGRISTRATION SYSTEM INC, MORTGAGE ELECTRONIC',
                                 'Registration Systems, Mortgage Electronic',
                                 'SY, MORTGAGE ELECTRONIC REGISTRATION',
                                 'SYSTEM INC, MORTGAGE ELECTRONIC REAGISTRATION',
                                 'SYSTEM INC., MORTGAGE ELECTRONIC REGISTRATION',
                                 "SYSTEM'S INC, MORTGAGE ELECTRONIC REGIS.",
                                 'SYSTEM, INC, MORTGAGE ELECTRONIC REGISTRATION',
                                 'SYSTEM, MORTGAGE ELECTRONIC  REGISTRAT',
                                 'SYSTEMS INC., MORTGAGE ELECTRONIC',
                                 'SYSTEMS, INC., MORTGAGE ELECTRONIC REGISTRATION',
                                 'SYSTEMS, MORTGAGE ELECTRONIC  REGISTRATION',
                                 'SYSTEMS, MORTGAGE ELECTRONIC REGISTRATI',
                                 'SYSTEMS, MORTGAGE ELECTRONIC REGISTRATION',
                                 'SYSTEMS,INC, MORTGAGE ELECTRONIC REGISTRATION',
                                 'SYSTEMS,INC., MORTGAGE ELECTRONIC REGISTRATION',
                                 
                                 'ELECTRONIC   REGISTATION  SYST, MORTGAGE',
                                 'ELECTRONIC  INC, MORTGAGE',
                                 'ELECTRONIC  REG. SYS.,  MORTGAGE',
                                 'ELECTRONIC MORT. REG. SYSTEM, .',
                                 'ELECTRONIC REG  SYS INC, MORTGAGE',
                                 'ELECTRONIC REGISTRATION , SHD MORTGAGE',
                                 'ELECTRONIC REGISTRATION SYSTEM, MORTGAGE',
                                 'ELECTRONIC REGISTRATION, MORTGAGE',
                                 'ELECTRONIC SYSTEMS, MORTGAGE',
                                 'ELECTRONIC, MORTGAGE',
                                 'JONATHAN EGHMAN, MTG. ELECTRONIC REGISTRATION S IN C/O',
                                 'MORTGAGE ELECTRONIC',
                                 'REGISRATION SYSTEM INC., MTG. ELECTRONIC',
                                 'REGISTRATION  SY, MORTOR ELECTRONIC',
                                 'REGISTRATION SYS. INC, MORTAGE  ELECTRONIC',
                                 'REGISTRATION SYSTEM INC., MORTAGE ELECTRONIC',
                                 'REGISTRATION SYSTEM, INC., MORTGAGE  ELECTRONIC',
                                 'REGISTRATION SYSTEMS, MORTGAGE  ELECTRONIC',
                                 'REGISTRATION SYSTEMS,INC, MORTGAGE  ELECTRONIC',
                                 'REGISTRATION SYSTEMS. INC., MORTGAGE  ELECTRONIC',
                                 'REGISTRATION, MORTGAGE  ELECTRONIC',
                                 'SYSTEM, ELECTRONIC REGISTRATION',
                                 'SYSTEMS INC., MTG. ELECTRONIC REGISTRATION',
                                 'SYSTEMS, INC, ELECTRONIC REGISTRATION',
                                 'SYSTEMS, MTG.ELECTRONIC REGISTRATION',
                                 'TROTT&TROTT ATTY., MTG.ELECTRONIC REGISTRATION SY IN C/O',
                                 'system inc., MTG.Electronic registration',
                                 'system llc, mtg.electronic registration',
                                 'system llc., mtg.electronic registration'],
        'FEDERAL NATIONAL MTG. A.' : ['ASSN, FEDERAL NATIONAL MTG',
                                      'ASSOC, FEDERAL NATIONAL MORTGAGE',
                                      'ASSOC.,  FEDERAL NATIONAL MTG.',
                                      'ASSOC., FEDERAL NATIONAL MORTGAGE',
                                      'ASSOC., FEDERAL NATIONAL MTG.',
                                      'ASSOCIATION, FEDERAL NATIONAL MORTGAGE',
                                      'FEDERAL NATIONAL MORTGAGE ASSO, *',
                                      'FEDERAL NATIONAL MORTGAGE ASSO, .',
                                      'FEDERAL NATIONAL MTG. ASSN., .',
                                      'MORT ASSOCIATES, FEDERAL NATIONAL',
                                      'MORTGAGE  ASS., FEDERAL NATIONAL',
                                      'MORTGAGE  ASSOC, FEDERAL NATIONAL',
                                      'MORTGAGE ASS, FEDERAL NATIONAL',
                                      'MORTGAGE ASSN., FEDERAL NATIONAL',
                                      'MORTGAGE ASSO., FEDERAL NATIONAL',
                                      'MORTGAGE ASSOC, FEDERAL NATIONAL',
                                      'MORTGAGE ASSOC. , FEDERAL NATIONAL',
                                      'MORTGAGE ASSOC., FEDERAL NATIONAL',
                                      'MORTGAGE ASSOCATION , FEDERAL NATIONAL',
                                      'MORTGAGE ASSOCIATION, FEDERAL NATIONAL',
                                      'MORTGAGE, FEDERAL NATIONAL',
                                      'MTG ASSOC., FEDERAL NATIONAL',
                                      'MTG, FEDERAL NATIONAL',
                                      'MTG. ASSOC., FEDERAL NATIONAL',
                                      'Mortgage Assoc., Federal National',
                                      'Mortgage Association, Federal National',
                                      'NORTGAGE ASSOCIATION, FEDERAL NATIONAL'],
            'BANK, HSBC' : ['BANK  USA, HSBC',
                            'BANK (USA), HSBC',
                            'BANK USA, HSBC',
                            'CORP III.HSBC  MTG SERVICES, HOUSEHOLD FINANCE',
                            'CORP III.HSBC MTG SERVICES, HOUSEHOLD FINANCE',
                            'MORTGAGE, HSBC',
                            'USA, HSBC BANK'],
            'SEPTEMBER PROPERTIES' : ['PROPERTIES, SEPTEMBER', 'PROPERTYS, SEPTEMBER', 'SEPTEMBER PROPERTIES, .'],
            'DETROIT NEIGHBORHOOD DEVELOPMENT' : ['CO, DET  NEIGHBORHOOD DEVELOPMENT',
                                    'CO, DETROIT NEIGHBOR HOOD DEVELOPMENT',
                                    'CO, DETROIT NEIGHBORHOOD DEVELOPMENT',
                                    'CORP., DETROIT NEIGHBORHOOD DEVELOPMENT',
                                    'DETROIT NEIGHBORHOOD DEV, NORTHWEST',
                                    'DETROIT NEIGHBORHOOD DEV., *',
                                    'DETROIT NEIGHBORHOOD DEVEL COR, 000000',
                                    'DETROIT NEIGHBORHOOD DEVELOPE, .',
                                    'DETROIT NEIGHBORHOOD DEVELOPME, *',
                                    'DETROIT NEIGHBORHOOD DVL. CORP, *',
                                    'DETROIT NEIGHBORHOOD DVLP., *',
                                    'DETROIT NEIGHBORHOOD SERV., *',
                                    'DETROIT NEIGHBORHOOD, NORTHWEST',
                                    'DEV. CORP., DETROIT NEIGHBORHOOD',
                                    'DEV., DETROIT NEIGHBORHOOD',
                                    'DEVEL0PMENT, DETROIT NEIGHBORHOOD',
                                    'DEVELOPEMENT CO., DETROIT NEIGHBORHOOD',
                                    'DEVELOPEMENT, DETROIT  NEIGHBORHOOD',
                                    'DEVELOPEMENT, DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENR CORPORATION, DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT  CO., DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT  CORP., DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT CO, DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT CO., DETROIT  NEIGHBORHOOD',
                                    'DEVELOPMENT CO., DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT CORP, DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT CORP., NORTHWEST DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT, DETROIT NEIGHBOR',
                                    'DEVELOPMENT, DETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT, NORTHWESTDETROIT NEIGHBORHOOD',
                                    'DEVELOPMENT.CO, DETROIT  NEIGHBORHOOD',
                                    'Detroit Neighborhood Dev. Co. , *',
                                    'Dev. Corp., Cass Corridor Neighborhood',
                                    'Development Co., Detroit Neighborhood',
                                    'Development Corp, Detroit Neighborhood',
                                    'Development Corp., Detroit Neighborhood',
                                    'Development, Detroit Neighborhood',
                                    'NEIGHBOR DEV., DETROIT',
                                    'NEIGHBORHOOD  DEV, NORTHWEST DETROIT',
                                    'NEIGHBORHOOD  DEV., NORTHWEST  DETROIT',
                                    'NEIGHBORHOOD  DEV., NORTHWEST DETROIT',
                                    "NEIGHBORHOOD DEV'T, DETROIT",
                                    'NEIGHBORHOOD DEV, DETROIT',
                                    'NEIGHBORHOOD DEV. CO., DETROIT',
                                    'NEIGHBORHOOD DEV., DETROIT',
                                    'NEIGHBORHOOD DEV., NORTHWEST  DETROIT',
                                    'NEIGHBORHOOD DEV., NORTHWEST DETROIT',
                                    'NEIGHBORHOOD DEVELOPMENT CO, DETROIT',
                                    'NEIGHBORHOOD DEVELOPMENT CORP, *',
                                    'NEIGHBORHOOD DEVELOPMENT, DETROIT',
                                    'NEIGHBORHOOD INC., CORE CITY',
                                    'NEIGHBORHOOD, *',
                                    'NEIGHBORHOOD, CORE CITY',
                                    'NEIGHBORHOOD, INC., CORE CITY',
                                    'NEIGHBORHOOD, NORTHWEST DETROIT',
                                    'NEIGHBORHOOD, SOUTHWEST ALLIANCE FOR',
                                    'NEIGHBORHOODS INC., CORE CITY',
                                    'NEIGHBORHOODS, CORE CITY',
                                    'NORTHWEST DETROIT NEIGHBORHOOD, .',
                                    'development co, detroit neighborhood'],
        'KAY BEE KAY PROPERTIES, .' : ['PROPERTIES L.L.C, KAY BEE KAY',
                                       'PROPERTIES, KAY BEE KAY',
                                       'Properties, LLC, Kay Bee Kay']
                                                    
                                      

    
}

misspellings_inv = {}
for key in misspellings.keys():
    for value in misspellings[key]:
        misspellings_inv[value] = key

df_fines['violator_name'] = df_fines['violator_name'].map(
    lambda name:
        misspellings_inv[name] if name in misspellings_inv.keys() 
        else name
)

df_fines.columns

Index(['ticket_id', 'agency_name', 'inspector_name', 'violator_name',
       'violation_street_number', 'violation_street_name',
       'violation_zip_code', 'mailing_address_str_number',
       'mailing_address_str_name', 'city', 'state', 'zip_code',
       'non_us_str_code', 'country', 'ticket_issued_date', 'hearing_date',
       'violation_code', 'violation_description', 'disposition', 'fine_amount',
       'admin_fee', 'state_fee', 'late_fee', 'discount_amount',
       'clean_up_cost', 'judgment_amount', 'payment_amount', 'balance_due',
       'payment_date', 'payment_status', 'collection_status',
       'grafitti_status', 'compliance_detail', 'compliance', 'ticket_year',
       'ticket_month', 'ticket_day', 'isMonday', 'isTuesday', 'isWednesday',
       'isThursday', 'isFriday', 'isSaturday', 'isSunday', 'isOwner'],
      dtype='object')

# df_fine_counter = df_fines.groupby('violator_name').sum()[['balance_due']].sort_values(by='balance_due', ascending=False)
# df_fine_counter.head(20)

selected = []
query = "KAY BEE KAY"
keys = df_fines['violator_name'].unique()
for key in keys:
    if query.lower() in key.lower():
        selected.append(key)
sorted(selected)

['KAY BEE KAY PROPERTIES, .']

df_joint = pd.merge(df_addresses, df_latlons,
                    how='inner', on='address')
df_joint = pd.merge(df_fines, df_joint,
                    how='inner', on='ticket_id')

len(df_fines.index)

42023

df_fines.head(5)

df_fines.iloc[1]

ticket_id                                                                 27586
agency_name                      Buildings, Safety Engineering & Env Department
inspector_name                                                 Williams, Darrin
violator_name                                          Michigan, Covenant House
violation_street_number                                                    4311
violation_street_name                                                   CENTRAL
violation_zip_code                                                          NaN
mailing_address_str_number                                               2959.0
mailing_address_str_name                                     Martin Luther King
city                                                                    Detroit
state                                                                        MI
zip_code                                                                  48208
non_us_str_code                                                             NaN
country                                                                     USA
ticket_issued_date                                          2004-04-23 12:30:00
hearing_date                                                     5/6/2005 13:30
violation_code                                                       61-63.0600
violation_description         Failed To Secure Permit For Lawful Use Of Buil...
disposition                                                         Responsible
fine_amount                                                                 750
admin_fee                                                                    20
state_fee                                                                    10
late_fee                                                                   75.0
discount_amount                                                               0
clean_up_cost                                                                 0
judgment_amount                                                           855.0
payment_amount                                                            780.0
balance_due                                                                75.0
payment_date                                                      6/2/2005 0:00
payment_status                                                                2
collection_status                                                           NaN
grafitti_status                                                             NaN
compliance_detail                      compliant by late payment within 1 month
compliance                                                                 True
ticket_year                                                                2004
ticket_month                                                                  4
ticket_day                                                                   23
isMonday                                                                  False
isTuesday                                                                 False
isWednesday                                                               False
isThursday                                                                False
isFriday                                                                   True
isSaturday                                                                False
isSunday                                                                  False
isOwner                                                                       0
Name: 1, dtype: object

#df_fines.corr(method='pearson')

len(df_fines.index)

42023

df_fine_counter=df_fines['violator_name'].value_counts().to_dict()
df_fines.head(10)
df_fine_counter

{'INVESTMENT, ACORN': 692,
 'BANK, WELLS FARGO': 156,
 'MORTGAGE ELECTRONIC': 138,
 'DETROIT NEIGHBORHOOD DEVELOPMENT': 131,
 'BANK, DEUTSCHE': 121,
 'MANAGEMENT CO., WINGATE': 62,
 'NEW YORK, BANK OF': 61,
 'BANK, JP MORGAN CHASE': 60,
 'FEDERAL NATIONAL MTG. A.': 59,
 'MILLER, JOHN': 50,
 'SNOW, GEORGE': 49,
 'KRAMER, KEITH': 41,
 'LDHA, BARCLAY': 38,
 'PACE, MICHAEL': 37,
 'STEHLIK, JERRY': 32,
 'ESTATES, EVERGREEN': 31,
 'Lasalle Bank National Association': 30,
 'RICHARDSON, JOHN': 30,
 'US Bank National Association': 29,
 'HUDSON, OTTMAR': 28,
 'VILLAS, GENESIS': 27,
 'ROSEN, NATHAN': 25,
 'COMMONS LDHA LP, CHENE PARK': 25,
 'LEONARD, JACK': 24,
 'KING VILLAGE, ALBERTA W.': 24,
 'BURTON, GERALD': 21,
 'BURRELL, RAYMOND': 21,
 'BURRELL, HELENE': 20,
 'WOLFE, KENNETH': 19,
 'MANAGEMENT, FOREST CITY': 19,
 'DETROIT HOUSING COMMISSION, *': 19,
 'CONSTRUCTION, REX': 19,
 'COLEMAN, HAROLD': 19,
 'GROUP, THE APARTMENT': 18,
 'BANK, WASHINGTON MUTUAL': 18,
 'PROPERTIES, HOSKINS': 18,
 'BUFFINGTON, DERONE': 18,
 'FORIEST, DONALD': 17,
 'FOREST PARK, FRIENDSHIP': 17,
 'APARTMENTS, CARLTON': 17,
 'GARDENS, SAVANNAH': 17,
 'BROYLES, JOE A': 16,
 'BAKEMAN, NORMAN': 16,
 'COMPANY, DETROIT LEASING': 16,
 'BANK, WACHOVIA': 15,
 'FRIENDSHIP FOREST PARK, *': 15,
 'SMITH, LUCILLE': 15,
 'JONES, ALVIN': 15,
 'HUFF, CHERYL': 15,
 'BROOKS, PENCYNTHIE': 15,
 'KANSY, JOSEPH': 15,
 'SOULAR, GEORGE': 14,
 'LANNING, ANTHONY L.': 14,
 'BRIKHO, MAYSOON': 14,
 'LARKINS, DEONE': 14,
 'nan': 14,
 'JACKSON, RAYFORD': 14,
 'APARTMENTS, WESTPARK': 14,
 'TURNBULL, HUGO': 14,
 'TODD, TYRONE': 14,
 'DYER, MARGIE': 14,
 'ROW INC., NOTTINGHAM': 13,
 'CONNER, VICKY': 13,
 'SHIFMAN, ALLEN': 13,
 'ROSE, MAURICE': 13,
 'SAMMUT, ELIZABETH': 13,
 'CALHOUN, ANDREW': 13,
 'MOORE, LEE V': 13,
 'DIXON, ROBERT': 13,
 'PROPERTIES INC., HOSKINS': 12,
 'CHAPMAN, LEON': 12,
 'CORPORATION, 1ST CITY': 12,
 'TREANOR, JOHN': 12,
 'APARTMENTS, TOWN SQUARE': 12,
 'PULLIAM, JAMES': 12,
 'ONE, BANK': 12,
 'JOHNSON, ROBERT': 12,
 'HICKMAN, ERMA': 12,
 'WILLIAMS, SAM': 12,
 'DEMBICKI, JOSEPH': 12,
 'BARNES, JOHN': 11,
 'JACKSON, CARLSON': 11,
 'MANOR, EMBASSY': 11,
 'PAPALE, MARK': 11,
 'ROSSER JR., ERWIN': 11,
 'HOLMES, EDDIE': 11,
 'RUPERT, HENRY': 11,
 'SHORES, TINA': 11,
 'INVESTMENT CO., REX': 11,
 'THOMPSON, PHILLIP': 11,
 'DANIELS, ROBERT': 11,
 'SANTARELLI, MARCO': 11,
 'BROWN, HAZEL': 11,
 'ROBINSON, JOHN': 11,
 'Kramer, Keith': 11,
 'WOODS, SAMUEL': 11,
 'PROPERTIES LLC, CUSIMANO BROS': 10,
 'LAIN, MARY': 10,
 'TOWNHOMES, PALMER COURT': 10,
 'WILSON, BARRY': 10,
 'FENNOY, GWEN': 10,
 'SACHS, RALPH': 10,
 'WILLIAMS, WOODROW': 10,
 'DINATALE, DONALD': 10,
 'Corp., Amurcon': 10,
 'NESBITT, LEVI': 10,
 'FINANCIAL SERVICES, MILLENNIUM': 10,
 'WILLIAMS, GREGORY': 10,
 'L D H A, BARCLAY': 10,
 'FLOYD, GREGORY': 10,
 'KIMBLE, AED': 10,
 'CO. INC., IMPERIAL MGMT': 9,
 'COUTILISH, ARISTIDIES': 9,
 'RAMSEY, EUGENE': 9,
 'DAVIS, REGINALD': 9,
 'SMITH, VINCENT': 9,
 'GORDON, JEANETTE': 9,
 'MOORE, MICHAEL': 9,
 'CUNNINGHAM, BRENDA': 9,
 'TREASURER, WAYNE COUNTY': 9,
 'MANAGEMENT, PREMIER': 9,
 'JORDAN, WILLIE': 9,
 'BEVERLY, LUTHER': 9,
 'GADSON, JAMES': 9,
 'ANDERSON, DONALD': 9,
 'WELLS FARGO BANK, .': 9,
 'JOHNSON, CHRISTOPHER': 9,
 'GADD, DONALD': 9,
 'HOGUE, SHIRLEY': 9,
 'DAVENPORT, ROSA B': 9,
 'WALLACE, RANDOLPH': 9,
 'WERKMEISTER, THOMAS': 9,
 'MEADOWS, FRIENDSHIP': 9,
 'L.D.H.A.L.P., Northlawn Apts.': 9,
 'LOZOYA, RAMON': 9,
 'irwin, john': 9,
 'LITTLEJOHN, WILLIAM D': 9,
 'PROPHET CHURCH, WORD OF THE': 9,
 'WILCOX, DONALD': 9,
 'PRICE, RICHARD': 9,
 'BREWART, WILLIAM': 9,
 'SANDERS, OTIS': 8,
 'COMMISSION, DETROIT HOUSING': 8,
 'FELLOWSHIP ESTATES LLC, .': 8,
 'LUKOWSKI, KAZ': 8,
 'LOWERY, DEON': 8,
 'ENERGY, DTE': 8,
 'DAVIS, ROBERT': 8,
 'MARSH, CAROLYN': 8,
 'CHAPPELL, BARBARA': 8,
 'FREEDMAN, JUDY': 8,
 'INVESTMENT CO, REX': 8,
 'JONES, EDDIE': 8,
 'JACKSON, CLARENCE': 8,
 'MARTIN, NAKISHA': 8,
 'BURRELL, RAYMOND C': 8,
 'BURRELL, RAYMOND C.': 8,
 'LLC, P J U': 8,
 'JACKSON, THEODORE': 8,
 'SNOW, GEORGE A': 8,
 'GREEN III, ALEX': 8,
 'SMITH, JOHN': 8,
 'MANAGEMENT, KEYSTONE': 8,
 'WOODGET, JAMES': 8,
 'LOANS, COUNTRYWIDE HOME': 8,
 'CLARK, KENNETH': 8,
 'MATHEW, GEORGE': 8,
 'M.E.R.S.I., .': 8,
 'CHAMBERS, REGINA': 8,
 'ASSOCIATES, FAIR': 8,
 'HILTON, PATRICK': 8,
 'MURRAY JR., WILLIE': 8,
 'MICHELS, ROBERT': 8,
 'GUTHERY, HELENA': 8,
 'SCOTT, DWIGHT': 8,
 'WILSON, ROY': 8,
 'JOHNSON, JOHN': 8,
 'management, perkins': 8,
 'HOBBS, JAMES': 8,
 'BANK, HSBC': 8,
 'Snow, George': 8,
 'Miller, John': 8,
 'LEASING CO., DETROIT': 8,
 'MCMURRAY, HERMAN': 8,
 'MBONU, JONAS': 8,
 'THOMPSON, LYNN': 8,
 'WHITE, FRANK': 8,
 'GARDNER, MICHAEL': 8,
 'REYNOLDS, GEORGE': 8,
 'HILL, MARZETTE': 8,
 'MONROE, ANTHONY': 7,
 'FOX, CLEMON': 7,
 'ALLEN, ROBERT': 7,
 'BANK, COMERICA': 7,
 'TURNER, JUAN': 7,
 'THOMAS, WILLIE': 7,
 'OUTREACH MINISTRIES, FREE INDEED': 7,
 'KELLY, RALPH': 7,
 'TURNER, ROBERT': 7,
 'ADAMS JR., ROBERT': 7,
 'WILSON, JOSEPH': 7,
 'HINES, RAYMOND': 7,
 'LARSON, ROBERT': 7,
 'CRAWFORD, WILLIE': 7,
 'MOORE, MARY': 7,
 'GOROSH, MARTIN': 7,
 'TATARIAN, MATTHEW': 7,
 'WILLIAMS, VICTOR': 7,
 'MURRAY, JAMES': 7,
 'DAVIS, JOHN': 7,
 'VAN LUVEN, MARION': 7,
 'HAISHA, ADIL': 7,
 'WILSON, JAMES': 7,
 'MARSHALL, GLORIA': 7,
 'KINGSLEY, THOMAS': 7,
 'IPOSU, STEPHEN': 7,
 'THOMPSON, MARILYN': 7,
 'JOHN, LAWRENCE': 7,
 'HEWITT, RAYMOND': 7,
 'COUNTS, ALMA': 7,
 'KARMO, JOSEPH': 7,
 'GROUP, THE FORMIDABLE': 7,
 'MCLAURIN, FRED': 7,
 'WHITE, WALTER': 7,
 'LLC, PJU': 7,
 'RISING, LOUISE': 7,
 'SMITH, JOSEPH': 7,
 'DONOVAN, MICHAEL': 7,
 'JANZEWSKI, ROBERT': 7,
 'MITCHELL, RICHARD': 7,
 'MGT CO., LOCKWOOD': 7,
 'COTTINGHAM, ROGER': 7,
 'WILLIAMS, EDDIE': 7,
 'HOMES LDHA, PROJECT': 7,
 'KIRBY, TRACY': 7,
 'COOPERATIVE, RALPH BUNCHE': 7,
 'ALAM, ASAAD': 7,
 'SALEM, AHMED': 7,
 'GROSS, JASON': 7,
 'moloian, aramis': 7,
 'AIGORO, SHAMS': 7,
 'DAVIS, SIDNEY': 7,
 'WEINER, SAMUEL': 7,
 'BARLOW, LINDA': 7,
 'PROPERTIES, A & M': 7,
 'LLC, ANATOLE': 7,
 'LACLEAR, JOYCE': 7,
 'MITCHELL, CLARENCE': 7,
 'COHEN, DAN': 7,
 'JOHNSON, LARRY': 7,
 'RIVERS, RONALD': 7,
 'YAZBECK, GHASSAN': 7,
 'SYED, NAVEED': 7,
 'WILLIAMS, EDWIN L': 7,
 'COLE, PHILLIP': 7,
 'JONES, DEBORAH': 7,
 'TINNON, CURTIS': 7,
 'WILLIAMS, MARCUS': 7,
 'MICHELS, ROSE MARIE': 7,
 'BANK, STANDARD FEDERAL': 6,
 'MOUNT, HERBERT': 6,
 'WILLIAMS, JAMES': 6,
 'JONES, EDWARD': 6,
 'HASENAU, JAMES': 6,
 'COX, BESSIE': 6,
 'INVESTMENT II, GREAT LAKES': 6,
 'DEVELOPMENT LLC, DAVIES': 6,
 'BROWN, HORACE': 6,
 'DAYNE, MICHAEL F.': 6,
 'WILLIAMS, WILLIE': 6,
 'THOMPSON, DAVID': 6,
 'CLARK, JAMES': 6,
 'STEELE JR., HOWARD': 6,
 'PROPERTIES, TREBUR': 6,
 'EDISON, DETROIT': 6,
 'MOORE, SHAWN': 6,
 'DUNLAP, DEBRA': 6,
 'DAVIS, REGINALD CLYDE': 6,
 'BROWN, ANNIE': 6,
 'BOYD, NARCISUS': 6,
 'MOSLEY, JACOB': 6,
 'GOLDBERGER, MEYER': 6,
 'SMITH, WILLIAM': 6,
 'DAYNE, MICHAEL': 6,
 'PROPERTIES LLC, JIMCO': 6,
 'Schwall, Edward': 6,
 'HAMMOUD, RAMZI': 6,
 'TANKSLEY, SYLVIA': 6,
 'JONES, JIMMIE': 6,
 'TURNBULL, HUGO K': 6,
 'KAMEL, CAROLE': 6,
 'BUCHANAN, RODNEY': 6,
 "INC., AMY'S": 6,
 'PILOT, ARNEE': 6,
 'NOWAKOWSKI, JOSEPH': 6,
 'REID, DWAYNE': 6,
 'TUGGLE, LESTER': 6,
 'LAVANT, BRYANT': 6,
 'HEATH, GARFIELD': 6,
 'HENDERSON, MARY': 6,
 'BRADLEY, TROY': 6,
 'YOUSIF, MIKE': 6,
 'ARMSTRONG, RAYMOND': 6,
 'ADAMS, AARON': 6,
 'HARDY, RONALD': 6,
 'GARY, CHRISTIAN': 6,
 'MCGEE, MICHAEL': 6,
 'COMMONS, CHENE PARK': 6,
 'DETROIT MERCY, UNIVERSITY OF': 6,
 'WOOD, LOWELL': 6,
 'MCCLINTON, ALFRED': 6,
 'REX CONSTRUCTION, *': 6,
 'JOHNSON, WILLIAM': 6,
 'Stehlik, Jerry': 6,
 'FETWIE, SIRAJ': 6,
 'SANDERS, MARCUS': 6,
 'TURNER, WALTER': 6,
 'PARKS, DERRICK': 6,
 'BASS, MARCUS': 6,
 'AYZIN, BORIS': 6,
 'PIIR, PETER': 6,
 'MACK, IRENE': 6,
 'MATHEW, MATHEW': 6,
 'BATCHELOR, GWENDOLYN': 6,
 'REYNOLDS, LELDON': 6,
 'CARPENTER, CLIFFORD': 6,
 'TAYLOR, ISAAC': 6,
 'WEBSTER, BERNARD': 6,
 'TENBROECK, CHARLES': 6,
 'KAGE, RICHARD M': 6,
 'MICHALAK, CHRIS': 6,
 'SELPH, WILLIE': 6,
 'SMITH, BILLY': 6,
 'INVESTMENT LLC, MOGHUL': 6,
 'HILL, DONALD': 6,
 'JOHNSON, BERNARD': 6,
 'NORTON, ERIC': 6,
 'BROOKS, JOHN': 6,
 'STEWART, ANNIE': 6,
 'WRIGHT, JAMES': 6,
 'TRICE JR., BENJAMIN': 6,
 'JOHNSON, WALTER': 6,
 'PEARSON, BECKWITH': 6,
 'HYDER, ALI': 6,
 'HUDSON, DORA': 6,
 'ROSMAN, STEPHEN': 6,
 'ROGERS, EUGENE': 6,
 'WILLIAMS, RUBY': 6,
 'JOHNSON, BRENDA': 6,
 'MARSHALL, DOROTHEA': 6,
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 'GREEN, GWENDOLYN': 4,
 'THOMAS, CHERYL': 4,
 'ZAGHIR, NADA': 4,
 'PATTAH, CALVIN': 4,
 'CORBELL, STANLEY': 4,
 'MARSHALL, BOBBIE F': 4,
 'JUDNICH, ROBERT': 4,
 'BRICK CO., HISTORIC': 4,
 'Salem, Ahmed': 4,
 'COPELAND, RONALD': 4,
 'REAL ESTATE CO., PHOENIX': 4,
 'PAYMON, MARY': 4,
 'BOWLSON, JAMARE': 4,
 'DETROIT LLC, RAC': 4,
 'OF AMERICA, MORTGAGE CORP': 4,
 'PULLIAM, MARY': 4,
 'MANAGEMENT INC., BLEVINS PROPERTY': 4,
 'HUFFMAN, PATRICIA': 4,
 'DAVIS, VERONICA': 4,
 'CUREATHERS, CLAUDINE': 4,
 'ONWURZURIKE, PHID': 4,
 'WHITED, DANNY': 4,
 'HICKS, GREGORY': 4,
 'MURRAY, MELVIN': 4,
 'WIDEMAN, DENNIS': 4,
 'CORPORATION, CHASE MANHATTAN MORTGAGE': 4,
 'RIGGINS, RUTH': 4,
 ...}

#Getting the different values and descriptions
numberOfTimes = df_fines['violation_description'].value_counts()
number = pd.DataFrame({'description':numberOfTimes.index,'times':numberOfTimes.values}).head(n=15) #e just want the first 15 because are the most relevant
plt.scatter(number['description'],number['times'])
plt.xticks(number['description'], rotation=-90)
for i, row in number.iterrows():
    plt.text(row['description'], row['times']+600, s= str(row['times']))
plt.show()

df_fine_counter_by_street=df_fines['violation_street_name'].value_counts().to_dict()
df_fine_counter_by_street
##len(df_fine_counter_by_street)

{'SEVEN MILE': 845,
 'MCNICHOLS': 682,
 'LIVERNOIS': 447,
 'EVERGREEN': 380,
 'FENKELL': 303,
 'JOY RD': 299,
 'WARREN': 290,
 'EIGHT MILE': 264,
 'PURITAN': 254,
 'GRAND RIVER': 251,
 'SCHAEFER': 244,
 'PLYMOUTH': 230,
 'GRAND BLVD': 227,
 'WYOMING': 222,
 'GRATIOT': 219,
 'CHICAGO': 216,
 'STOUT': 214,
 'FORRER': 212,
 'SUSSEX': 204,
 'PLAINVIEW': 203,
 'STOEPEL': 201,
 'WINTHROP': 198,
 'VAN DYKE': 191,
 'OUTER DRIVE': 186,
 'PATTON': 184,
 'STAHELIN': 182,
 'MANSFIELD': 181,
 'MEYERS RD': 180,
 'WOODWARD AVE': 179,
 'FIELDING': 179,
 'FAUST': 179,
 'HARPER': 177,
 'SANTA ROSA': 174,
 'VAUGHAN': 173,
 'HEYDEN': 171,
 'CLEMENTS': 169,
 'ST MARYS': 169,
 'MARK TWAIN': 168,
 'AUBURN': 166,
 'ASHTON': 165,
 'DAVISON': 163,
 'GREENFIELD': 162,
 'NORTHLAWN': 158,
 'WARD': 156,
 'GRANDVILLE': 156,
 'MINOCK': 155,
 'RUTHERFORD': 150,
 'SCHOOLCRAFT': 149,
 'APPOLINE': 143,
 'TIREMAN': 142,
 'MENDOTA': 140,
 'MANOR': 139,
 'PIERSON': 139,
 'ASBURY PARK': 138,
 'INDIANA': 136,
 'JEFFERSON': 136,
 'HAZELWOOD': 136,
 'ARCHDALE': 135,
 'GREENVIEW': 134,
 'METTETAL': 133,
 'CHERRYLAWN': 130,
 'HARTWELL': 129,
 'PINEHURST': 129,
 'JAMES COUZENS': 129,
 'BURT RD': 127,
 'CHALMERS': 125,
 'ROSEMONT': 125,
 'EUCLID': 122,
 'SORRENTO': 122,
 'PRAIRIE': 122,
 'LAHSER': 121,
 'WHITCOMB': 120,
 'KENTUCKY': 120,
 'ROBSON': 118,
 'BIRWOOD': 117,
 'CHEYENNE': 117,
 'CONANT': 116,
 'LAUDER': 116,
 'WISCONSIN': 116,
 'MACK': 116,
 'DEXTER': 114,
 'WILFRED': 113,
 'MEMORIAL': 113,
 'FERGUSON': 112,
 'OHIO': 112,
 'ROSELAWN': 112,
 'TRINITY': 111,
 'KENTFIELD': 111,
 'CORTLAND': 110,
 'MONICA': 109,
 'MONTROSE': 109,
 'FULLERTON': 108,
 'GREENLAWN': 108,
 'HUBBELL': 108,
 'ARTESIAN': 108,
 'WOODMONT': 108,
 'CLAIRMOUNT': 107,
 'LITTLEFIELD': 107,
 'CHENE': 106,
 'COYLE': 105,
 'GRAND': 105,
 'CENTRAL': 105,
 'BEACONSFIELD': 104,
 'FLANDERS': 103,
 'TYLER': 100,
 'WESTWOOD': 100,
 'VIRGINIA PARK': 99,
 'NOTTINGHAM': 99,
 'VERNOR': 96,
 'FORT ST': 96,
 'FREELAND': 95,
 'HELEN': 94,
 'LINWOOD': 93,
 'WESTBROOK': 93,
 'STEEL': 92,
 'MT ELLIOTT': 92,
 'ABINGTON AVE': 91,
 'SCHOENHERR': 91,
 'LAKEWOOD': 90,
 'MARLOWE': 89,
 'BRAILE': 89,
 'GLENFIELD': 89,
 'TUXEDO': 89,
 'RUNYON': 87,
 'BENTLER': 87,
 'MOROSS': 86,
 'NEWPORT': 86,
 'PREVOST': 86,
 'ST LOUIS': 86,
 'STRATHMOOR': 85,
 'TULLER': 85,
 'PENROD': 84,
 'LYNDON': 83,
 'BRACE': 83,
 'FENMORE': 83,
 'BUENA VISTA': 83,
 'WHITTIER': 82,
 'BILTMORE': 82,
 'ILENE': 82,
 'MICHIGAN AVE': 81,
 'ASHLAND': 81,
 'RUTLAND': 80,
 'ELMHURST': 80,
 'PIEDMONT': 78,
 'WESTPHALIA': 78,
 'TACOMA': 78,
 'ALTER': 76,
 'HICKORY': 76,
 'STATE FAIR': 76,
 'LENOX': 74,
 'ORLEANS': 74,
 'JOHN R': 73,
 'STANSBURY': 73,
 'TERRY': 72,
 'SOUTHFIELD': 71,
 'HULL': 71,
 'MOENART': 70,
 'HARDING': 70,
 'LONGACRE': 69,
 'CANTON': 68,
 'WILDEMERE': 68,
 'GRANDMONT': 68,
 'SYRACUSE': 68,
 'HOUSTON-WHITTIER': 68,
 'CALVERT': 68,
 'MARX': 68,
 'AMERICAN': 67,
 'PHILADELPHIA': 67,
 'ARDMORE': 67,
 'PASADENA': 66,
 'CLOVERLAWN': 65,
 '14TH ST': 65,
 'BLOOM': 64,
 'EASTLAWN': 64,
 'MORANG': 64,
 'WEBB': 64,
 'CHELSEA': 62,
 'GILCHRIST': 62,
 'MCDOUGALL': 62,
 'ANGLIN': 62,
 'WARWICK': 62,
 'ALCOY': 60,
 'CEDARGROVE': 60,
 'CRANE': 59,
 'GLASTONBURY': 59,
 'ROCKDALE': 59,
 'FAIRPORT': 59,
 'LINDSAY': 59,
 'MAIDEN': 58,
 'WAYBURN': 58,
 'KELLY RD': 58,
 'GLYNN CT': 57,
 'IRVINGTON': 57,
 'PREST': 57,
 'MANNING': 57,
 'BELVIDERE': 57,
 'COLUMBUS': 56,
 'BRENTWOOD': 56,
 'BUCKINGHAM': 56,
 'KILBOURNE': 56,
 'CADILLAC SQUARE': 55,
 'ST ANTOINE': 55,
 'CHALFONTE': 55,
 'PELKEY': 55,
 'HOLCOMB': 54,
 'LAKEPOINTE': 54,
 'RICHTON': 54,
 'GUILFORD': 54,
 'LAKEVIEW': 54,
 'ALBION': 54,
 'WEST PARKWAY': 54,
 'LESLIE': 54,
 'BUFFALO': 54,
 'WASHBURN': 52,
 'DRESDEN': 52,
 'MADDELEIN': 52,
 'EVANSTON': 51,
 'MURRAY HILL': 51,
 'DOLPHIN': 51,
 'BURLINGAME': 51,
 'BALFOUR': 51,
 'BEWICK': 50,
 'RUSSELL': 50,
 'WHITHORN': 50,
 'LAWNDALE': 50,
 'GABLE': 50,
 'BARLOW': 50,
 'GALLAGHER': 50,
 'PEMBROKE': 50,
 'GLADSTONE': 50,
 'MITCHELL': 49,
 'WAVERLY': 49,
 'SEWARD': 49,
 'TURNER': 49,
 'KERCHEVAL': 49,
 'MAPLERIDGE': 49,
 'VINEWOOD': 49,
 'MONTCLAIR': 48,
 'MONTE VISTA': 48,
 'MILITARY': 48,
 'DREXEL': 48,
 'FISCHER': 48,
 'LONGVIEW': 48,
 'ALBANY ST': 47,
 'COPLIN': 47,
 'SPRINGWELLS': 47,
 'OMIRA': 47,
 'CHAPEL': 47,
 'SECOND': 47,
 'KENDALL': 47,
 'FINDLAY': 47,
 'FIELD': 47,
 'OAKFIELD': 47,
 'GOULBURN': 47,
 'MACKAY': 46,
 'GRIGGS': 46,
 'GLENDALE': 46,
 'WALTHAM': 46,
 'MARTINDALE': 45,
 'TEPPERT': 45,
 'LINNHURST': 45,
 'TROESTER': 45,
 'TRACEY': 45,
 'PINGREE': 45,
 'EASTWOOD': 45,
 'HOLLYWOOD': 45,
 'LA SALLE BLVD': 44,
 'LOTHROP': 44,
 'QUINCY': 44,
 'TAYLOR': 44,
 'JOANN': 44,
 'ALMA': 44,
 'ELMIRA': 44,
 'FORDHAM': 44,
 'HOOVER': 44,
 'CARLIN': 43,
 'CARTER': 43,
 'GODDARD': 43,
 'MONTEREY': 43,
 'PARK GROVE': 43,
 'BRYDEN': 43,
 'LAFAYETTE': 43,
 'DICKERSON': 42,
 'ATKINSON': 42,
 'FENELON': 42,
 'HAVERHILL': 42,
 'SEYMOUR': 41,
 'MANISTIQUE': 41,
 'MARLBOROUGH': 41,
 'LEMAY': 41,
 'ELLSWORTH': 41,
 'NOVARA': 41,
 'LESURE': 41,
 'HASSE': 41,
 'CHRYSLER': 41,
 'BELMONT': 41,
 'HARLOW': 41,
 'WOODHALL': 41,
 'IROQUOIS': 41,
 'FLEMING': 41,
 'SNOWDEN': 41,
 'GREYDALE': 41,
 'HAMBURG': 41,
 'CANFIELD': 40,
 'PROMENADE': 40,
 'BLAINE': 40,
 'CORBETT': 40,
 'TOWNSEND': 40,
 'FENTON': 40,
 'SARATOGA': 40,
 'ST AUBIN': 39,
 'THIRD': 39,
 'DECATUR': 39,
 'DEAN': 39,
 'YOUNG': 39,
 'LONYO': 39,
 'PILGRIM': 39,
 'STRASBURG': 38,
 'BEAVERLAND': 38,
 'CAMPBELL': 38,
 'ELMDALE': 38,
 'HAYES': 38,
 'JUNCTION': 38,
 'LOUISIANA': 38,
 'LUMPKIN': 38,
 'ANNOTT': 37,
 'LAWTON': 37,
 'FAIRFIELD': 37,
 'ALLENDALE': 37,
 'DEVONSHIRE': 37,
 'STURTEVANT': 37,
 'RIOPELLE': 37,
 'BAYSIDE': 37,
 'KEYSTONE': 37,
 'LAMPHERE': 36,
 'LAWRENCE': 36,
 'CALDWELL': 36,
 'COMMONWEALTH': 36,
 'FRANKFORT': 36,
 'CADIEUX': 36,
 'BEATRICE': 36,
 'NEVADA': 35,
 'CAMDEN': 35,
 'GREINER': 35,
 'ETHEL': 35,
 'CHATSWORTH': 35,
 'IVANHOE': 35,
 'SAVANNAH': 35,
 'MARTIN': 35,
 'FAIRCREST': 35,
 'MUIRLAND': 35,
 'VICKSBURG': 34,
 'SEEBALDT': 34,
 'CONNER': 34,
 'TARNOW': 34,
 'LAMONT': 34,
 'BRADFORD': 34,
 'CASPER': 33,
 'FERRY': 33,
 'KLINGER': 33,
 'WOODBINE': 33,
 'JANE': 33,
 'LIBERAL': 33,
 'TRUMBULL': 33,
 'COURVILLE': 33,
 'GREELEY': 33,
 'JUSTINE': 32,
 'RADEMACHER': 32,
 'PICKFORD': 32,
 'BURGESS': 32,
 'KENSINGTON': 32,
 'PACKARD': 32,
 'BURNS': 32,
 'ROCHELLE': 32,
 'JOSEPH CAMPAU': 32,
 'ALEXANDRINE': 32,
 'BEDFORD': 32,
 'HIGHLAND': 31,
 'CASS': 31,
 'HAWTHORNE': 31,
 'WASHINGTON BLVD': 31,
 'WINSTON': 31,
 'FOREST': 31,
 'SHIELDS': 31,
 'SALEM': 31,
 'ROSA PARKS BLVD': 31,
 'MORAN': 31,
 'BLACKSTONE': 31,
 'CRUSE': 30,
 'EDSEL': 30,
 'OAKMAN BLVD': 30,
 'CHATHAM': 30,
 'STOTTER': 30,
 'CASCADE': 30,
 'VOTROBECK DR': 30,
 'WADE': 30,
 'ANDOVER': 29,
 'ALGONQUIN': 29,
 'DWYER': 29,
 'ARLINGTON': 29,
 'BELAND': 29,
 'TOLEDO': 29,
 'ROSSINI DRIVE': 29,
 'GARLAND': 29,
 'DACOSTA': 29,
 'CARSON': 29,
 'WOODMERE': 28,
 'COLLINGWOOD': 28,
 'CLARENDON': 28,
 'GRANDY': 28,
 'HURLBUT': 28,
 'TELEGRAPH': 28,
 'BRAMELL': 28,
 'FARMBROOK': 28,
 'ROWE': 28,
 'ROBINWOOD': 28,
 'GREEN': 28,
 'MARGARET': 28,
 'BAYLIS': 28,
 'BALDWIN': 27,
 'PENNSYLVANIA': 27,
 'SEYBURN': 27,
 'CHAREST': 27,
 'LAPPIN': 27,
 'BROADSTREET': 27,
 'OAKLAND': 27,
 'GRIXDALE': 27,
 'BINDER': 27,
 'NAVY': 27,
 'MT VERNON': 26,
 'LARKINS': 26,
 'EWALD CIRCLE': 26,
 'EDISON': 26,
 'OREGON': 26,
 'CAMERON': 26,
 'SANFORD': 26,
 'BROADWAY': 26,
 'SAN JUAN': 26,
 'LARNED': 26,
 'REVERE': 26,
 'SCOTTEN': 26,
 'BEECHWOOD': 26,
 'FAIRVIEW': 26,
 'YOSEMITE': 25,
 'PETOSKEY': 25,
 'THADDEUS': 25,
 '24TH ST': 25,
 'MAYFIELD': 25,
 'QUINN': 25,
 'FRENCH RD': 25,
 'RIVARD': 25,
 'PALMER': 25,
 'SEMINOLE': 25,
 'WYKES': 25,
 '30TH ST': 25,
 'RADNOR': 25,
 'CARRIE': 25,
 '28TH ST': 25,
 'MCCLELLAN': 24,
 'CONLEY': 24,
 'GRISWOLD': 24,
 'FREDERICK': 24,
 'GLENWOOD': 24,
 'NEFF': 24,
 'BURNSIDE': 24,
 'WABASH': 24,
 'WHITNEY': 24,
 'BAGLEY': 24,
 'ROSEDALE CT': 24,
 '15TH ST': 23,
 '25TH ST': 23,
 'SHERWOOD': 23,
 'HAZLETT': 23,
 'PHILIP': 23,
 'CABOT': 23,
 'CONCORD': 23,
 'RACINE': 23,
 'SPRING GARDEN': 23,
 'CICOTTE': 23,
 'HARVARD RD': 23,
 'MCGRAW': 23,
 'GREENDALE': 23,
 'BRINGARD DR': 23,
 'LUMLEY': 23,
 'RATHBONE': 22,
 'CARLISLE': 22,
 '23RD ST': 22,
 'LIDDESDALE': 22,
 'HANNA': 22,
 'KNODELL': 22,
 'VANCOUVER': 22,
 'LENORE': 22,
 'EASTBURN': 22,
 'DESOTO': 22,
 'HOGARTH': 22,
 'PARKER': 22,
 'MAXWELL': 22,
 'MOUND': 22,
 'YELLOWSTONE': 22,
 'ROSEMARY': 22,
 'HEREFORD': 22,
 'BENITEAU': 21,
 'BOSTON BLVD': 21,
 'KENILWORTH': 21,
 'DERBY': 21,
 'BRUSH': 21,
 'WOODROW': 21,
 'PRINCETON': 21,
 'SOMERSET': 21,
 'LANE': 21,
 'CAHALAN': 20,
 'YACAMA': 20,
 'CAVALRY': 20,
 'KEATING': 20,
 'COVENTRY': 20,
 'HAMPSHIRE': 20,
 'CORAM': 20,
 'DEACON': 20,
 'CHENLOT': 20,
 'ROHNS': 20,
 'LANNETTE': 20,
 'CASGRAIN': 20,
 'CHOPIN': 20,
 'LILAC': 20,
 'CROCUSLAWN': 20,
 'BUHR': 20,
 'WILSHIRE': 20,
 'WOODROW WILSON': 20,
 'BEAUBIEN': 20,
 'MAPLEWOOD': 19,
 'SANTA BARBARA': 19,
 'MAINE': 19,
 'ALGONAC': 19,
 'LOG CABIN': 19,
 'WORMER': 19,
 'SHERIDAN': 19,
 'WILLIS': 19,
 'WHITMORE RD': 19,
 'LAMOTHE': 19,
 'GILBERT': 19,
 'DORIS': 19,
 'MONTANA': 19,
 'EXETER': 19,
 'BLUEHILL': 19,
 'HEALY': 18,
 'RYAN': 18,
 'BURNETTE': 18,
 'WILLETTE': 18,
 'HANCOCK': 18,
 'CARDONI': 18,
 'ROCHESTER': 18,
 '16TH ST': 18,
 '17TH ST': 18,
 'MCKINNEY': 18,
 'WARRINGTON': 18,
 'ALGER': 18,
 'FLORIDA': 18,
 'CLIFF': 18,
 'PARK': 18,
 'ST CLAIR': 18,
 'GRAYTON': 18,
 'TRENTON': 18,
 'HOLMUR': 18,
 'EPWORTH': 18,
 'FARNSWORTH': 17,
 'DANBURY': 17,
 'LEE PL': 17,
 'HAGUE': 17,
 'SENATOR': 17,
 'RIVERVIEW': 17,
 'LILLIBRIDGE': 17,
 'ELECTRIC': 17,
 'SPENCER': 17,
 'LA SALLE GARDENS': 17,
 'AUGUST': 17,
 'MORRELL': 17,
 'WOODINGHAM': 17,
 'CECIL': 17,
 'GRAYFIELD': 17,
 'DIX': 16,
 'ROXBURY': 16,
 'LIEBOLD': 16,
 'VAN COURT': 16,
 'DUMFRIES': 16,
 'COOPER': 16,
 'OTSEGO': 16,
 'BLACKMOOR': 16,
 'GARTNER': 16,
 'TRAVERSE': 16,
 'INVERNESS': 16,
 'INGLIS': 16,
 'LANSING': 16,
 'ANNABELLE': 16,
 'PACIFIC': 16,
 'KENNEBEC': 16,
 'EDMORE DR': 16,
 'AVON': 16,
 'INFANTRY': 16,
 'MEDBURY': 16,
 'WATERMAN': 16,
 'COLLEGE': 16,
 'ELMER': 16,
 'OLDTOWN': 16,
 'PINEWOOD': 16,
 'HOUGHTON': 16,
 'FERDINAND': 16,
 'BEGOLE': 16,
 'MINNESOTA': 15,
 'MELVILLE': 15,
 'BRADEN': 15,
 'ELIZABETH': 15,
 'APPLETON': 15,
 'FAIRMOUNT DR': 15,
 'LONGWORTH': 15,
 'YORKSHIRE': 15,
 'DRAGOON': 15,
 'RIDGEWOOD': 15,
 'HAZELRIDGE': 15,
 'GRAY': 15,
 '35TH ST': 15,
 'HOMER': 15,
 'SUNSET': 15,
 'DUBOIS': 15,
 'COOLEY': 15,
 'PEARL': 15,
 'FERRIS': 15,
 'FERRY PARK': 15,
 'REGENT DR': 14,
 'THREE MILE DR': 14,
 'MAPLELAWN': 14,
 '31ST ST': 14,
 'MARSTON': 14,
 'LODEWYCK': 14,
 'WHITFIELD': 14,
 'CHARLEVOIX': 14,
 'CURTIS': 14,
 'HILLSBORO': 14,
 'SHARON': 14,
 'WEXFORD': 14,
 'VIRGIL': 14,
 'GENESSEE': 14,
 'ROSSITER': 14,
 'MARYLAND': 14,
 'LONGFELLOW': 14,
 'UNIVERSITY PL': 14,
 'WOODLAND': 14,
 'MAJESTIC': 14,
 'PARKSIDE': 14,
 'SARENA': 14,
 'DANIELS': 14,
 'KIRBY': 14,
 'WHITTAKER': 14,
 'THATCHER': 14,
 'CAPITOL': 14,
 'HARRELL': 14,
 'POE': 13,
 'SANTA CLARA': 13,
 'HOLMES': 13,
 'ANDERDON': 13,
 'WESTMINSTER': 13,
 'COVINGTON': 13,
 'CRAWFORD': 13,
 'LELAND': 13,
 'CHANDLER PARK DR': 13,
 'SUNDERLAND RD': 13,
 'BUCHANAN': 13,
 'NARDIN': 13,
 'CHARLEMAGNE': 13,
 'FITZPATRICK': 13,
 'OAKWOOD': 13,
 'WESSON': 13,
 'LINSDALE': 13,
 'COLFAX': 13,
 'NORMANDY': 13,
 'WHITEWOOD': 13,
 'PARKWOOD': 13,
 'CLARKDALE': 13,
 'M L KING BLVD': 13,
 'CLAYBURN': 13,
 'MINDEN': 13,
 'SENECA': 13,
 'CHARLESTON': 12,
 'FLOYD': 12,
 'JOHN C LODGE': 12,
 'OLIVET': 12,
 'TENNESSEE': 12,
 'FILBERT': 12,
 'BELLEVUE': 12,
 'CLIPPERT': 12,
 'MAGNOLIA': 12,
 'KENMOOR': 12,
 'FILER': 12,
 'MARSEILLES': 12,
 'ADAMS AVE': 12,
 'GUNSTON': 12,
 'MELDRUM': 12,
 'PROCTOR': 12,
 'HUNTINGTON': 12,
 'BASSETT': 12,
 'MILWAUKEE': 12,
 'WESTERN': 12,
 'MONTGOMERY': 12,
 'LOVETT': 12,
 'ENGLEWOOD': 11,
 'LABELLE': 11,
 'LORETTO': 11,
 'NASHVILLE': 11,
 'ASHLEY': 11,
 'OGDEN': 11,
 'CEDARLAWN': 11,
 'SPOKANE': 11,
 'FOLEY': 11,
 'SCOVEL PL': 11,
 'SAWYER': 11,
 'JULIAN': 11,
 'SMART': 11,
 'THEODORE': 11,
 'WHITEHILL': 11,
 'OAKDALE': 11,
 'LARCHMONT': 11,
 'BARBARA': 11,
 'HILDALE': 11,
 'AUDUBON': 11,
 'NUERNBERG': 11,
 'LAING': 11,
 'FRONTENAC': 11,
 'WITHERELL': 11,
 'EDSEL FORD': 11,
 'COPLAND': 11,
 'HAROLD': 11,
 'BARLUM': 11,
 'UNDERWOOD': 11,
 'CLARK ST': 11,
 'KING': 11,
 'HALLECK': 10,
 'MARCUS': 10,
 'EVANS': 10,
 'ADELINE ST': 10,
 'DEQUINDRE': 10,
 'DUNDEE': 10,
 'MCKINLEY': 10,
 'MERRICK': 10,
 'LORAINE': 10,
 'MCQUADE': 10,
 'FORTUNE': 10,
 'WHEELER': 10,
 'COLUMBIA': 10,
 'RADCLIFFE': 10,
 'COLLINGHAM': 10,
 'ALDEN': 10,
 'PETERBORO': 10,
 'CONGRESS': 10,
 'GEORGIA': 10,
 'RIAD': 10,
 'HOUSE': 10,
 'DALE': 10,
 'MCLEAN': 10,
 'ELSMERE': 10,
 'MAPLEVIEW': 10,
 'MASON PL': 10,
 'LUTHER': 10,
 'HOYT': 10,
 'SANTA MARIA': 10,
 'ALLONBY': 10,
 'BELLETERRE': 10,
 'ROGERS': 10,
 'MOLENA': 10,
 'CHRISTY': 10,
 'LANCASHIRE': 10,
 'CLIFTON': 10,
 'WENDELL': 10,
 'JOSEPHINE': 9,
 'MCCORMICK': 9,
 'ELMWOOD': 9,
 'CHURCHILL': 9,
 'EWERS': 9,
 'MISSOURI': 9,
 'BERKSHIRE': 9,
 'ORANGELAWN': 9,
 'CHESTER': 9,
 'PETER HUNT': 9,
 'GRINNELL': 9,
 'PORTER': 9,
 'WARING': 9,
 'STAIR': 9,
 'ROOSEVELT': 9,
 'CADET': 9,
 'WEST POINT': 9,
 'BARRETT': 9,
 'PENNINGTON': 9,
 'CHARLES': 9,
 'NORTHWESTERN': 9,
 'WHEELOCK': 9,
 'VIOLETLAWN': 9,
 'GRANDVIEW': 9,
 'SHREWSBURY': 9,
 'LEIDICH': 9,
 'KANE': 9,
 '18TH ST': 9,
 'HUBBARD': 9,
 'MANDERSON': 9,
 'WETHERBY': 9,
 'LEICESTER CT': 9,
 'IOWA': 9,
 'HUBER': 9,
 'DIVISION': 8,
 'EDWARD': 8,
 'LANIER': 8,
 'WALDO': 8,
 'SPRINGLE': 8,
 'MARION': 8,
 'HARNED': 8,
 'LANSDOWNE': 8,
 'EDINBOROUGH': 8,
 'IDAHO': 8,
 'NAUMANN': 8,
 '29TH': 8,
 'STOCKWELL': 8,
 'CHRISTIANCY': 8,
 'HAMILTON': 8,
 'HUMPHREY': 8,
 'HARMON': 8,
 'KIMBERLY CT': 8,
 'GREENSBORO': 8,
 'VERMONT': 8,
 '52ND ST': 8,
 'BEALS': 8,
 'NORTHFIELD': 8,
 'DAWES': 8,
 'FORD': 8,
 'PUTNAM': 8,
 'LINCOLN': 8,
 'MOFFAT': 8,
 'GATESHEAD': 8,
 'MCPHERSON': 8,
 'HAZELTON': 8,
 'FLORENCE': 8,
 'CHAMBERLAIN': 8,
 'KITCHENER': 8,
 'GREUSEL': 8,
 'BRIMSON': 8,
 'MCKINSTRY': 8,
 'ROGGE': 8,
 'DARTMOUTH': 8,
 'WESTMORELAND': 8,
 'PAYTON': 8,
 'GARFIELD': 7,
 'JOHN KRONK': 7,
 'PFENT': 7,
 'ALFRED': 7,
 'WAGER': 7,
 'EUREKA': 7,
 'MELBOURNE': 7,
 'ALMONT': 7,
 'LONGWOOD': 7,
 'MAY': 7,
 'NORWALK': 7,
 'GOODWIN': 7,
 'HENDRIE': 7,
 'BARHAM': 7,
 'RIVERDALE DR': 7,
 'HARRISON': 7,
 'ERSKINE': 7,
 'BENTON': 7,
 'MARGARETA': 7,
 'OAK DR': 7,
 'BORDEAU': 7,
 'GREYFRIARS': 7,
 'BANGOR': 7,
 'DELAWARE': 7,
 'DOROTHY': 7,
 'ST PATRICK': 7,
 'ANNCHESTER': 7,
 'EASTERN PL': 7,
 'PORTLANCE': 7,
 'DAYTON': 7,
 'CATHEDRAL': 7,
 'ENGLESIDE': 7,
 'FRISBEE': 7,
 'GLADYS': 7,
 'MANILA': 7,
 'HARTFORD': 7,
 'PRESTON': 7,
 'WAVENEY': 7,
 'LAWLEY': 7,
 'SPARLING': 7,
 'ARDEN PARK': 7,
 'GREENWAY': 6,
 'WREFORD': 6,
 'CARBONDALE': 6,
 'BRINKER': 6,
 'CALUMET': 6,
 'ST HEDWIG': 6,
 'CASTLETON': 6,
 'MALCOLM': 6,
 'TERRELL': 6,
 'RENO': 6,
 'IRON': 6,
 'VISGER': 6,
 'ADELAIDE ST': 6,
 'PATRICIA': 6,
 'COLONIAL': 6,
 'JAMESON': 6,
 'KARL': 6,
 'CHANDLER': 6,
 'TEMPLE': 6,
 'FIVE POINTS': 6,
 'SUNNYSIDE': 6,
 'CLAYTON': 6,
 'WHITEHEAD': 6,
 'NEWBERN': 6,
 'HANOVER': 6,
 'BRITAIN': 6,
 'ANVIL': 6,
 'MOTOR DRIVE': 6,
 'GLENCO': 6,
 'HONORAH': 6,
 'OMAHA': 6,
 'DUPREY': 6,
 'SHOEMAKER': 6,
 'FOURTH': 6,
 'HARBAUGH': 6,
 'SPARTA': 6,
 'VAN BUREN': 6,
 'WOODLAWN': 6,
 'PELOUZE': 6,
 'KELLER': 6,
 'SHIAWASSEE': 6,
 'HAMMOND': 6,
 'RICHARD': 6,
 'PARKHURST': 6,
 'CLARION': 6,
 'WEAVER': 6,
 'MORLEY': 6,
 'STOCKTON': 6,
 'ST JOHN': 6,
 'ST LAWRENCE': 6,
 'MCDONALD': 6,
 'RAVENSWOOD': 6,
 '51ST ST': 6,
 'MERTON': 6,
 'AURORA': 6,
 'DALRYMPLE': 6,
 'DAILEY CT': 5,
 'AVERY': 5,
 'SOLVAY': 5,
 'GOLDNER': 5,
 'HIBBARD': 5,
 'ALAMEDA ST': 5,
 'MARNE': 5,
 'BEAUFAIT': 5,
 'NORCROSS': 5,
 'NAVAHOE': 5,
 'BESSEMORE': 5,
 'PEERLESS': 5,
 'ELIOT': 5,
 'MEADE': 5,
 'MONROE': 5,
 'DEARBORN': 5,
 'BELDEN': 5,
 'GRUEBNER': 5,
 'SHAFTSBURY': 5,
 'BLAKE': 5,
 'JEROME': 5,
 'BRAINARD': 5,
 'MILTON': 5,
 'SAVERY': 5,
 'EVERTS': 5,
 'GOLDEN GATE': 5,
 'SIRE': 5,
 'BALTIMORE': 5,
 'GROVE': 5,
 'PRINCE HALL DR': 5,
 'EDGETON': 5,
 'EDGEWOOD': 5,
 'MELLON': 5,
 'SUPERIOR': 5,
 'KEELSON DR': 5,
 'DUANE': 5,
 'LOGAN': 5,
 'KIPLING': 5,
 'FREDA': 5,
 'BADGER': 5,
 'PALMETTO': 5,
 'HILL': 5,
 'PRENTIS': 5,
 'GITRE': 5,
 'EAGLE': 5,
 'VASSAR': 5,
 'NORTHROP': 5,
 'CINCINNATI': 5,
 'DIVERSEY': 5,
 'FRANKLIN': 5,
 'CHURCH': 5,
 'WESTFIELD': 5,
 '32ND ST': 5,
 'HILLCREST': 5,
 'STANTON': 5,
 'POST': 5,
 'WINDER': 5,
 ...}

# import matplotlib.pyplot as plt
# plt.scatter(x=df_latlons['lon'], y=df_latlons['lat'])
# plt.show()
import plotly.express as px
import pandas as pd
df_latlons.dropna(
    axis=0,
    how='any',
   # thresh=None,
    subset=None,
    inplace=True
)

color_scale = [(0, 'orange'), (1,'red')]

fig = px.scatter_mapbox(df_latlons, 
                        lat="lat", 
                        lon="lon", 
                       # hover_name="Address", 
                        #hover_data=["Address", "Listed"],
                        #color="Listed",
                       # color_continuous_scale=color_scale,
                        #size="Listed",
                        zoom=8, 
                        height=800,
                        width=800)

fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

#df_fines[['ticket_id', 'disposition', 'compliance', 'compliance_detail', 'collection_status', 'payment_status']]
df_joint.columns

Index(['ticket_id', 'agency_name', 'inspector_name', 'violator_name',
       'violation_street_number', 'violation_street_name',
       'violation_zip_code', 'mailing_address_str_number',
       'mailing_address_str_name', 'city', 'state', 'zip_code',
       'non_us_str_code', 'country', 'ticket_issued_date', 'hearing_date',
       'violation_code', 'violation_description', 'disposition', 'fine_amount',
       'admin_fee', 'state_fee', 'late_fee', 'discount_amount',
       'clean_up_cost', 'judgment_amount', 'payment_amount', 'balance_due',
       'payment_date', 'payment_status', 'collection_status',
       'grafitti_status', 'compliance_detail', 'compliance', 'ticket_year',
       'ticket_month', 'ticket_day', 'isMonday', 'isTuesday', 'isWednesday',
       'isThursday', 'isFriday', 'isSaturday', 'isSunday', 'isOwner',
       'address', 'lat', 'lon'],
      dtype='object')

df_fines['inspector_name'].value_counts()

inspector_name
Doetsch, James         1931
Gailes, Orbie J        1781
Legge, Gerald          1739
O'Neal, Claude         1726
Hayes, Billy J         1714
                       ... 
Clark, Marcel             1
Hischke, William          1
Crowder, Michael          1
Thomas, Duane             1
Gibson, Christopher       1
Name: count, Length: 100, dtype: int64

#inertias = []
#columns = ['lat','lon']

#for i in range(100,250):
#    kmeans = KMeans(n_clusters=i)
#    kmeans.fit(df_joint[columns])
#    inertias.append(kmeans.inertia_)

#plt.plot(range(100,250), inertias, marker='o')
#plt.title('Elbow method')
#plt.xlabel('Number of clusters')
#plt.ylabel('Inertia')
#plt.show()

import random


data_for_clustering = df_joint[['lat', 'lon']]


kmeans = KMeans(n_clusters=75)
kmeans.fit(data_for_clustering[['lat', 'lon']]) 



df_joint['cluster'] = kmeans.labels_

payment_rates = df_joint.groupby('cluster')['compliance'].mean().reset_index()
payment_rates = payment_rates.rename(columns={'compliance':'Rate'})
payment_rates.sort_values(by='Rate',ascending=False)

mapa_poligon = folium.Map(data_for_clustering=[df_joint['lat'].mean(), df_joint['lon'].mean()], zoom_start=10)
mapa_points= folium.Map(data_for_clustering=[df_joint['lat'].mean(), df_joint['lon'].mean()], zoom_start=10)


colores_poligon= ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
colores_points= ['#' + ''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(10)]
#poligon_map
for cluster_label in range(75):
    color = colores_points[cluster_label % 10]  
    points = df_joint[kmeans.labels_ == cluster_label][['lat', 'lon']].values.tolist()
    folium.Polygon(locations=points, color=color, fill=True, fill_opacity=0.3).add_to(mapa_poligon)

    center = np.mean(points, axis=0)
    
    folium.Marker(location=[center[0], center[1]],
                  icon=folium.DivIcon(html=f'<div style="font-size: 10pt; color: black;">{cluster_label}</div>')).add_to(mapa_poligon)

#points_map

for lat, lon, cluster in zip(df_joint['lat'], df_joint['lon'], kmeans.labels_):
    folium.CircleMarker(
        location=[lat, lon],
        radius=5,
        color=None,
        fill=True,
        fill_opacity=0.7,
        fill_color= colores_points[cluster%10]
    ).add_to(mapa_points)

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning:

The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning

mapa_points

mapa_poligon

payment_rates

df_joint['Rate'].max()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File /usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py:3790, in Index.get_loc(self, key)
   3789 try:
-> 3790     return self._engine.get_loc(casted_key)
   3791 except KeyError as err:

File index.pyx:152, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:181, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7080, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7088, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Rate'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[39], line 1
----> 1 df_joint['Rate'].max()

File /usr/local/lib/python3.10/dist-packages/pandas/core/frame.py:3896, in DataFrame.__getitem__(self, key)
   3894 if self.columns.nlevels > 1:
   3895     return self._getitem_multilevel(key)
-> 3896 indexer = self.columns.get_loc(key)
   3897 if is_integer(indexer):
   3898     indexer = [indexer]

File /usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py:3797, in Index.get_loc(self, key)
   3792     if isinstance(casted_key, slice) or (
   3793         isinstance(casted_key, abc.Iterable)
   3794         and any(isinstance(x, slice) for x in casted_key)
   3795     ):
   3796         raise InvalidIndexError(key)
-> 3797     raise KeyError(key) from err
   3798 except TypeError:
   3799     # If we have a listlike key, _check_indexing_error will raise
   3800     #  InvalidIndexError. Otherwise we fall through and re-raise
   3801     #  the TypeError.
   3802     self._check_indexing_error(key)

KeyError: 'Rate'

print(kmeans.get_params)

<bound method BaseEstimator.get_params of KMeans(n_clusters=75)>

from sklearn.tree import export_graphviz


clf = DecisionTreeClassifier()

dtv = df_joint[['fine_amount','late_fee','discount_amount','isMonday', 'isTuesday', 'isWednesday',
       'isThursday', 'isFriday', 'isSaturday', 'isSunday', 'isOwner']]

payStatus = df_joint[['compliance']]

train_x, test_x, train_y, test_y  = train_test_split(dtv,payStatus,test_size=0.3,random_state=1)


clf = clf.fit(train_x,train_y)

prediction = clf.predict(test_x)

print("Accuracy:",metrics.accuracy_score(test_y, prediction))

dot_data = StringIO()
export_graphviz(clf, out_file=dot_data,  
                filled=True, rounded=True,
                special_characters=True,feature_names = dtv.columns,class_names=['0','1'])
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
graph.write_png('finesTree.png')
Image(graph.create_png())

Accuracy: 0.9152789950335962

#Getting the different values and descriptions
numberOfTimes = df_fines['violation_description'].value_counts()
number = pd.DataFrame({'description':numberOfTimes.index,'times':numberOfTimes.values}).head(n=15) #e just want the first 15 because are the most relevant
plt.hist2d(number['description'],number['times'],bins= (15,15))
# plt.colorbar()
pl.show()
fin

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb Cell 43 line 4
      <a href='vscode-notebook-cell:/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb#X54sZmlsZQ%3D%3D?line=1'>2</a> numberOfTimes = df_fines['violation_description'].value_counts()
      <a href='vscode-notebook-cell:/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb#X54sZmlsZQ%3D%3D?line=2'>3</a> number = pd.DataFrame({'description':numberOfTimes.index,'times':numberOfTimes.values}).head(n=15) #e just want the first 15 because are the most relevant
----> <a href='vscode-notebook-cell:/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb#X54sZmlsZQ%3D%3D?line=3'>4</a> plt.hist2d(number['description'],number['times'],bins= (15,15))
      <a href='vscode-notebook-cell:/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb#X54sZmlsZQ%3D%3D?line=4'>5</a> # plt.colorbar()
      <a href='vscode-notebook-cell:/home/susi/universidad/dianamarca/Data_Science/Project/DataScienceOnDetroit/project1.ipynb#X54sZmlsZQ%3D%3D?line=5'>6</a> pl.show()

File ~/.local/lib/python3.10/site-packages/matplotlib/pyplot.py:3283, in hist2d(x, y, bins, range, density, weights, cmin, cmax, data, **kwargs)
   3269 @_copy_docstring_and_deprecators(Axes.hist2d)
   3270 def hist2d(
   3271     x: ArrayLike,
   (...)
   3281     **kwargs,
   3282 ) -> tuple[np.ndarray, np.ndarray, np.ndarray, QuadMesh]:
-> 3283     __ret = gca().hist2d(
   3284         x,
   3285         y,
   3286         bins=bins,
   3287         range=range,
   3288         density=density,
   3289         weights=weights,
   3290         cmin=cmin,
   3291         cmax=cmax,
   3292         **({"data": data} if data is not None else {}),
   3293         **kwargs,
   3294     )
   3295     sci(__ret[-1])
   3296     return __ret

File ~/.local/lib/python3.10/site-packages/matplotlib/__init__.py:1465, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
   1462 @functools.wraps(func)
   1463 def inner(ax, *args, data=None, **kwargs):
   1464     if data is None:
-> 1465         return func(ax, *map(sanitize_sequence, args), **kwargs)
   1467     bound = new_sig.bind(ax, *args, **kwargs)
   1468     auto_label = (bound.arguments.get(label_namer)
   1469                   or bound.kwargs.get(label_namer))

File ~/.local/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7191, in Axes.hist2d(self, x, y, bins, range, density, weights, cmin, cmax, **kwargs)
   7098 @_preprocess_data(replace_names=["x", "y", "weights"])
   7099 @_docstring.dedent_interpd
   7100 def hist2d(self, x, y, bins=10, range=None, density=False, weights=None,
   7101            cmin=None, cmax=None, **kwargs):
   7102     """
   7103     Make a 2D histogram plot.
   7104 
   (...)
   7188       `.colors.PowerNorm`.
   7189     """
-> 7191     h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range,
   7192                                        density=density, weights=weights)
   7194     if cmin is not None:
   7195         h[h < cmin] = None

File ~/.local/lib/python3.10/site-packages/numpy/lib/twodim_base.py:808, in histogram2d(x, y, bins, range, density, weights)
    806     xedges = yedges = asarray(bins)
    807     bins = [xedges, yedges]
--> 808 hist, edges = histogramdd([x, y], bins, range, density, weights)
    809 return hist, edges[0], edges[1]

File ~/.local/lib/python3.10/site-packages/numpy/lib/histograms.py:1003, in histogramdd(sample, bins, range, density, weights)
   1000 if bins[i] < 1:
   1001     raise ValueError(
   1002         '`bins[{}]` must be positive, when an integer'.format(i))
-> 1003 smin, smax = _get_outer_edges(sample[:,i], range[i])
   1004 try:
   1005     n = operator.index(bins[i])

File ~/.local/lib/python3.10/site-packages/numpy/lib/histograms.py:322, in _get_outer_edges(a, range)
    320 else:
    321     first_edge, last_edge = a.min(), a.max()
--> 322     if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
    323         raise ValueError(
    324             "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
    326 # expand empty range to avoid divide by zero

TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

#correlations = df_fines.corr()
#plt.figure(figsize=(10,10))
#sns.heatmap(correlations[1:10],annot=True,linewidths=0.05)
#sns.heatmap(correlations[2:20],annot=True,linewidths=0.05)

payment status	value assigned
NO PAYMENT APPLIED	0
PARTIAL PAYMENT APPLIED	1
PAID IN FULL	2

Data Science and Machine Learning¶

Property fines of maintenance in Detroid¶

12.12.2023¶

Introduction¶

Blight fines dataset¶

Goal¶

Data init¶

Data Cleaning¶

`violator_name` clean up¶

Combine the data¶

Data exploration¶

Header of the DataFrame¶

How many fines there are?¶

Count numbers of fines by `violator_name`¶

Counts of most common fine types¶

Which streets have most of the fines?¶

Where were the tickets issued?¶

Which addresses recieved the most tickets? (Common offenders)¶

Number of fines by inspector¶

Decision tree to know if a fine is going to be checked¶

Testing playground¶

disposition	value assigned
Not responsible by Dismissal	Not responsible
Not responsible by City Dismissal	Not responsible
Not responsible by Determination	Not responsible
Responsible by Default	Responsible
Responsible by Determination	Responsible
Responsible by Admission	Responsible
PENDING JUDGMENT	omitted
SET-ASIDE (PENDING JUDGMENT)	omitted

	ticket_id	agency_name	inspector_name	violator_name	violation_street_number	violation_street_name	violation_zip_code	mailing_address_str_number	mailing_address_str_name	city	...	ticket_month	ticket_day	isMonday	isTuesday	isWednesday	isThursday	isFriday	isSaturday	isSunday	isOwner
7	18735	Buildings, Safety Engineering & Env Department	Williams, Darrin	Rafee Auto Services L.L.C., RAF	8228	MT ELLIOTT	NaN	8228.0	Mt. Elliott	Detroit	...	6	16	False	False	True	False	False	False	False	1
8	18733	Buildings, Safety Engineering & Env Department	Williams, Darrin	Rafee Auto Services L.L.C., RAF	8228	MT ELLIOTT	NaN	8228.0	Mt. Elliott	Detroit	...	6	16	False	False	True	False	False	False	False	1
14	18978	Buildings, Safety Engineering & Env Department	Williams, Darrin	TLC Hand Car Wash, a/k/a	9425	VAN DYKE	NaN	9425.0	Van Dyke	Detroit	...	8	23	True	False	False	False	False	False	False	1
18	18746	Buildings, Safety Engineering & Env Department	Williams, Darrin	Klenk, Dean	14715	KLENK	NaN	14715.0	Klenk	Detroit	...	10	7	False	False	False	True	False	False	False	1
21	26846	Buildings, Safety Engineering & Env Department	Williams, Darrin	Al-Musawi, Doha	16331	WARREN	NaN	16331.0	W. Warren	Detroit	...	10	13	False	False	True	False	False	False	False	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
62436	87032	Department of Public Works	Lewis, Velinda	Harris, Tonie	19696	OMIRA	NaN	19696.0	Omira	Detroit	...	8	4	False	False	False	False	True	False	False	1
62452	87040	Department of Public Works	Lewis, Velinda	Williams-Dann, Myrtis Lee	19127	OMIRA	NaN	19127.0	Omira	Detroit	...	8	4	False	False	False	False	True	False	False	1
62455	87205	Department of Public Works	Bell, Maydell	MALKI, SHAMOUN	415	FERNHILL	NaN	415.0	FERNHILL	DETROIT	...	8	4	False	False	False	False	True	False	False	1
62464	87110	Department of Public Works	Lewis, Velinda	Agnitch, James J.	19692	OMIRA	NaN	19692.0	Omira	Detroit	...	8	4	False	False	False	False	True	False	False	1
62476	86384	Department of Public Works	Hayes, Billy J	MASON, RODNEY M	17144	BIRWOOD	NaN	17144.0	BIRWOOD	DETROIT	...	8	4	False	False	False	False	True	False	False	1

	ticket_id	agency_name	inspector_name	violator_name	violation_street_number	violation_street_name	violation_zip_code	mailing_address_str_number	mailing_address_str_name	city	...	ticket_month	ticket_day	isMonday	isTuesday	isWednesday	isThursday	isFriday	isSaturday	isSunday	isOwner
0	22056	Buildings, Safety Engineering & Env Department	Sims, Martinzie	INVESTMENT INC., MIDWEST MORTGAGE	2900	TYLER	NaN	3.0	S. WICKER	CHICAGO	...	3	16	False	True	False	False	False	False	False	0
1	27586	Buildings, Safety Engineering & Env Department	Williams, Darrin	Michigan, Covenant House	4311	CENTRAL	NaN	2959.0	Martin Luther King	Detroit	...	4	23	False	False	False	False	True	False	False	0
5	22046	Buildings, Safety Engineering & Env Department	Sims, Martinzie	KASIMU, UKWELI	6478	NORTHFIELD	NaN	2755.0	E. 17TH	LOG BEACH	...	5	1	False	False	False	False	False	True	False	0
6	18738	Buildings, Safety Engineering & Env Department	Williams, Darrin	Deerwood Development Group Inc, Deer	8027	BRENTWOOD	NaN	476.0	Garfield	Clinton	...	6	14	True	False	False	False	False	False	False	0
7	18735	Buildings, Safety Engineering & Env Department	Williams, Darrin	Rafee Auto Services L.L.C., RAF	8228	MT ELLIOTT	NaN	8228.0	Mt. Elliott	Detroit	...	6	16	False	False	True	False	False	False	False	1

	cluster	Rate
0	0	0.080402
1	1	0.069519
2	2	0.067416
3	3	0.103030
4	4	0.142857
...	...	...
70	70	0.060000
71	71	0.062201
72	72	0.146341
73	73	0.000000
74	74	0.117284

Data Science and Machine Learning¶

Property fines of maintenance in Detroid¶

12.12.2023¶

Introduction¶

Blight fines dataset¶

Goal¶

Data init¶

Data Cleaning¶

violator_name clean up¶

Combine the data¶

Data exploration¶

Header of the DataFrame¶

How many fines there are?¶

Count numbers of fines by violator_name¶

Counts of most common fine types¶

Which streets have most of the fines?¶

Where were the tickets issued?¶

Which addresses recieved the most tickets? (Common offenders)¶

Number of fines by inspector¶

Decision tree to know if a fine is going to be checked¶

Testing playground¶

`violator_name` clean up¶

Count numbers of fines by `violator_name`¶